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of the Workshop Neology and Large Language Models","10.63317\u002F42jjvod2wgsc","http:\u002F\u002Fwww.lrec-conf.org\u002Fproceedings\u002Flrec2026\u002Fworkshops\u002Fneollm\u002F2026.neollm-1.0.pdf","neollm|ws",[2303,2304,2305,2308,2311],{"given_name":1194,"surname":1195},{"given_name":1475,"surname":1476},{"given_name":2306,"surname":2307},"Florentina","Armaselu",{"given_name":2309,"surname":2310},"Chaya","Liebeskind",{"given_name":2312,"surname":2313},"Barbara","McGillivray",{"workshop_id":2315,"year":7,"full_workshop_id":2316,"proceedings_title":2317,"paperCount":815,"doi":2318,"pdf_url":2319,"venue_ids":2320,"publisher":13,"editors":2321,"conference_name":33,"conference_acronym":34,"conference_number":35,"conference_location":36,"conference_city":37,"conference_country":38,"conference_start_date":39,"conference_end_date":40},"nlp4ecology","lrec2026_ws_nlp4ecology","Proceedings of the 2nd Workshop on Ecology, Environment, and Natural Language 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NLPerspectives","10.63317\u002F5a3bvdkzb6f7","http:\u002F\u002Fwww.lrec-conf.org\u002Fproceedings\u002Flrec2026\u002Fworkshops\u002Fnlperspectives\u002F2026.nlperspectives-1.0.pdf",[2341,2342,2343,2344,2347],{"given_name":1533,"surname":1534},{"given_name":1524,"surname":1525},{"given_name":1527,"surname":1528},{"given_name":2345,"surname":2346},"Elisa","Leonardelli",{"given_name":1536,"surname":1537},{"workshop_id":2349,"year":7,"full_workshop_id":2350,"proceedings_title":2351,"paperCount":485,"doi":2352,"pdf_url":2353,"venue_ids":2354,"publisher":13,"editors":2355,"conference_name":33,"conference_acronym":34,"conference_number":35,"conference_location":36,"conference_city":37,"conference_country":38,"conference_start_date":39,"conference_end_date":40},"nonliteral","lrec2026_ws_nonliteral","Proceedings of Learning Non-Literal Expressions with Small Data @ LREC 2026","10.63317\u002F24t598e89qez","http:\u002F\u002Fwww.lrec-conf.org\u002Fproceedings\u002Flrec2026\u002Fworkshops\u002Fnonliteral\u002F2026.nonliteral-1.0.pdf","nonliteral|ws",[2356,2359],{"given_name":2357,"surname":2358},"Markus","Egg",{"given_name":2360,"surname":2361},"Valia","Kordoni",{"workshop_id":2363,"year":7,"full_workshop_id":2364,"proceedings_title":2365,"paperCount":2366,"doi":2367,"pdf_url":2368,"venue_ids":2369,"publisher":13,"editors":2370,"conference_name":33,"conference_acronym":34,"conference_number":35,"conference_location":36,"conference_city":37,"conference_country":38,"conference_start_date":39,"conference_end_date":40},"nslp","lrec2026_ws_nslp","Proceedings of Natural Scientific Language Processing (NSLP) @ LREC 2026",29,"10.63317\u002F44i27tid8nim","http:\u002F\u002Fwww.lrec-conf.org\u002Fproceedings\u002Flrec2026\u002Fworkshops\u002Fnslp\u002F2026.nslp-1.0.pdf","nslp|ws",[2371,2372,2375,2378,2381],{"given_name":717,"surname":718},{"given_name":2373,"surname":2374},"Stefan","Dietze",{"given_name":2376,"surname":2377},"Danilo","Dessi",{"given_name":2379,"surname":2380},"Diana","Maynard",{"given_name":2382,"surname":2383},"Sonja","Schimmler",{"workshop_id":794,"year":7,"full_workshop_id":2385,"proceedings_title":2386,"paperCount":2387,"doi":2388,"pdf_url":2389,"venue_ids":1548,"publisher":13,"editors":2390,"conference_name":33,"conference_acronym":34,"conference_number":35,"conference_location":36,"conference_city":37,"conference_country":38,"conference_start_date":39,"conference_end_date":40},"lrec2026_ws_osact","The 7th Workshop on Open-Source Arabic Corpora and Processing Tools (OSACT7) with 5 Shared Tasks",43,"10.63317\u002F55nvfe53k6fq","http:\u002F\u002Fwww.lrec-conf.org\u002Fproceedings\u002Flrec2026\u002Fworkshops\u002Fosact\u002F2026.osact-1.0.pdf",[2391,2392,2393],{"given_name":1551,"surname":1552},{"given_name":2073,"surname":2074},{"given_name":2394,"surname":2395},"Saad","Ezzini",{"workshop_id":800,"year":7,"full_workshop_id":2397,"proceedings_title":2398,"paperCount":541,"doi":2399,"pdf_url":2400,"venue_ids":1568,"publisher":13,"editors":2401,"conference_name":33,"conference_acronym":34,"conference_number":35,"conference_location":36,"conference_city":37,"conference_country":38,"conference_start_date":39,"conference_end_date":40},"lrec2026_ws_parlaclarin","Proceedings of the ParlaCLARIN V Workshop on Interoperability, Multilinguality, and Multimodality in Parliamentary Corpora","10.63317\u002F2gcgvfpyafm6","http:\u002F\u002Fwww.lrec-conf.org\u002Fproceedings\u002Flrec2026\u002Fworkshops\u002Fparlaclarin\u002F2026.parlaclarin-1.0.pdf",[2402,2403,2406],{"given_name":373,"surname":1574},{"given_name":2404,"surname":2405},"Vincent","Vandeghinste",{"given_name":1576,"surname":2407},"Bodron",{"workshop_id":990,"year":7,"full_workshop_id":2409,"proceedings_title":2131,"paperCount":2410,"doi":2411,"pdf_url":2412,"venue_ids":1583,"publisher":13,"editors":2413,"conference_name":33,"conference_acronym":34,"conference_number":35,"conference_location":36,"conference_city":37,"conference_country":38,"conference_start_date":39,"conference_end_date":40},"lrec2026_ws_politicalnlp",30,"10.63317\u002F382p55orpsvc","http:\u002F\u002Fwww.lrec-conf.org\u002Fproceedings\u002Flrec2026\u002Fworkshops\u002Fpoliticalnlp\u002F2026.politicalnlp-1.0.pdf",[],{"workshop_id":2415,"year":7,"full_workshop_id":2416,"proceedings_title":2417,"paperCount":815,"doi":2418,"pdf_url":2419,"venue_ids":2420,"publisher":13,"editors":2421,"conference_name":33,"conference_acronym":34,"conference_number":35,"conference_location":36,"conference_city":37,"conference_country":38,"conference_start_date":39,"conference_end_date":40},"pressmint","lrec2026_ws_pressmint","Proceedings of the First Workshop on Creating Interoperable Corpora of Historical Newspapers","10.63317\u002F4xmf6mt4ovnj","http:\u002F\u002Fwww.lrec-conf.org\u002Fproceedings\u002Flrec2026\u002Fworkshops\u002Fpressmint\u002F2026.pressmint-1.0.pdf","pressmint|ws",[2422,2425,2428],{"given_name":2423,"surname":2424},"Maciej","Ogrodniczuk",{"given_name":2426,"surname":2427},"Petya","Osenova",{"given_name":2429,"surname":2430},"Tanja","Wissik",{"workshop_id":806,"year":7,"full_workshop_id":2432,"proceedings_title":2433,"paperCount":469,"doi":2434,"pdf_url":2435,"venue_ids":1602,"publisher":13,"editors":2436,"conference_name":33,"conference_acronym":34,"conference_number":35,"conference_location":36,"conference_city":37,"conference_country":38,"conference_start_date":39,"conference_end_date":40},"lrec2026_ws_rail","Proceedings of Resources for African Indigenous Languages (RAIL) 2026 @ LREC 2026","10.63317\u002F44hkfj5cg3wf","http:\u002F\u002Fwww.lrec-conf.org\u002Fproceedings\u002Flrec2026\u002Fworkshops\u002Frail\u002F2026.rail-1.0.pdf",[],{"workshop_id":2438,"year":7,"full_workshop_id":2439,"proceedings_title":2440,"paperCount":645,"doi":2441,"pdf_url":2442,"venue_ids":2443,"publisher":13,"editors":2444,"conference_name":33,"conference_acronym":34,"conference_number":35,"conference_location":36,"conference_city":37,"conference_country":38,"conference_start_date":39,"conference_end_date":40},"rapid6mentalai","lrec2026_ws_rapid6mentalai","Proceedings of the Sixth Resources and ProcessIng of linguistic, para-linguistic and extra-linguistic Data from people with various forms of cognitive\u002Fpsychiatric\u002Fdevelopmental impairments in cooperation with the MENTAL.ai consortium","10.63317\u002F54scnv3cy8x7","http:\u002F\u002Fwww.lrec-conf.org\u002Fproceedings\u002Flrec2026\u002Fworkshops\u002Frapid6mentalai\u002F2026.rapid6mentalai-1.0.pdf","rapid6mentalai|ws",[2445,2446,2448,2451,2452,2454],{"given_name":1624,"surname":1625},{"given_name":1630,"surname":2447},"Themistocleous",{"given_name":2449,"surname":2450},"Gaël","Dias",{"given_name":1627,"surname":1628},{"given_name":1639,"surname":2453},"Öhman",{"given_name":2455,"surname":2456},"Sebastião","Pais",{"workshop_id":2458,"year":7,"full_workshop_id":2459,"proceedings_title":2460,"paperCount":712,"doi":2461,"pdf_url":2462,"venue_ids":2463,"publisher":13,"editors":2464,"conference_name":33,"conference_acronym":34,"conference_number":35,"conference_location":36,"conference_city":37,"conference_country":38,"conference_start_date":39,"conference_end_date":40},"readixtsar","lrec2026_ws_readixtsar","Proceedings of the Joint Workshop on Readability and Text Simplification (READIxTSAR) @ LREC 2026","10.63317\u002F3odyoa9tpigg","http:\u002F\u002Fwww.lrec-conf.org\u002Fproceedings\u002Flrec2026\u002Fworkshops\u002Freadixtsar\u002F2026.readixtsar-1.0.pdf","readixtsar|ws",[2465,2468,2470,2472,2474,2475,2478,2481,2484,2485],{"given_name":2466,"surname":2467},"Matthew","Shardlow",{"given_name":1723,"surname":2469},"François",{"given_name":384,"surname":2471},"Amaro",{"given_name":690,"surname":2473},"Baptista",{"given_name":1652,"surname":1653},{"given_name":2476,"surname":2477},"Eugénio","Ribeiro",{"given_name":2479,"surname":2480},"Horacio","Saggion",{"given_name":2482,"surname":2483},"Regina","Stodden",{"given_name":1655,"surname":1656},{"given_name":1650,"surname":1649},{"workshop_id":2487,"year":7,"full_workshop_id":2488,"proceedings_title":2489,"paperCount":678,"doi":2490,"pdf_url":2491,"venue_ids":2492,"publisher":13,"editors":2493,"conference_name":33,"conference_acronym":34,"conference_number":35,"conference_location":36,"conference_city":37,"conference_country":38,"conference_start_date":39,"conference_end_date":40},"resourceful","lrec2026_ws_resourceful","The Fourth Workshop on Resources and Representations for Under-Resourced Languages and Domains (RESOURCEFUL 2026)","10.63317\u002F3mcee7ktdfxn","http:\u002F\u002Fwww.lrec-conf.org\u002Fproceedings\u002Flrec2026\u002Fworkshops\u002Fresourceful\u002F2026.resourceful-1.0.pdf","resourceful|ws",[],{"workshop_id":826,"year":7,"full_workshop_id":2495,"proceedings_title":2496,"paperCount":836,"doi":2497,"pdf_url":2498,"venue_ids":2499,"publisher":13,"editors":2500,"conference_name":33,"conference_acronym":34,"conference_number":35,"conference_location":36,"conference_city":37,"conference_country":38,"conference_start_date":39,"conference_end_date":40},"lrec2026_ws_signlang","Proceedings of the LREC 2026 12th Workshop on the Representation and Processing of Sign Languages: Language in Motion","10.63317\u002F4zjm486botgq","http:\u002F\u002Fwww.lrec-conf.org\u002Fproceedings\u002Flrec2026\u002Fworkshops\u002Fsignlang\u002F2026.signlang-1.0.pdf","signlang|ws",[2501,2502,2503,2504,2507,2508],{"given_name":1717,"surname":1716},{"given_name":1720,"surname":1719},{"given_name":1723,"surname":1722},{"given_name":2505,"surname":2506},"Julie","A. Hochgesang",{"given_name":1729,"surname":1728},{"given_name":616,"surname":1731},{"workshop_id":1030,"year":7,"full_workshop_id":2510,"proceedings_title":2511,"paperCount":957,"doi":2512,"pdf_url":2513,"venue_ids":1738,"publisher":13,"editors":2514,"conference_name":33,"conference_acronym":34,"conference_number":35,"conference_location":36,"conference_city":37,"conference_country":38,"conference_start_date":39,"conference_end_date":40},"lrec2026_ws_sigul","Proceedings of the SIGUL 2026 Joint Workshop with ELE, EURALI, and DCLRL \"Towards Inclusivity and Equality: Language Resources and Technologies for Under-Resourced and Endangered Languages","10.63317\u002F3x5d49bm2yjm","http:\u002F\u002Fwww.lrec-conf.org\u002Fproceedings\u002Flrec2026\u002Fworkshops\u002Fsigul\u002F2026.sigul-1.0.pdf",[2515,2516,2517,2518,2519,2520,2523,2524,2525],{"given_name":1246,"surname":1247},{"given_name":67,"surname":68},{"given_name":1745,"surname":1746},{"given_name":1741,"surname":1742},{"given_name":687,"surname":688},{"given_name":2521,"surname":2522},"Constantine","Lignos",{"given_name":1256,"surname":1257},{"given_name":1772,"surname":2167},{"given_name":717,"surname":718},{"workshop_id":2527,"year":7,"full_workshop_id":2528,"proceedings_title":2529,"paperCount":771,"doi":2530,"pdf_url":2531,"venue_ids":2532,"publisher":13,"editors":2533,"conference_name":33,"conference_acronym":34,"conference_number":35,"conference_location":36,"conference_city":37,"conference_country":38,"conference_start_date":39,"conference_end_date":40},"slide","lrec2026_ws_slide","Proceedings of the Workshop on Structured Linguistic Data and Evaluation (SLiDE)","10.63317\u002F2ncrhaxfvhi4","http:\u002F\u002Fwww.lrec-conf.org\u002Fproceedings\u002Flrec2026\u002Fworkshops\u002Fslide\u002F2026.slide-1.0.pdf","slide|ws",[2534,2537,2540,2543,2546],{"given_name":2535,"surname":2536},"Germany)","Erhard Hinrichs (Tübingen University",{"given_name":2538,"surname":2539},"Sweden)","Joakim Nivre (Uppsala University",{"given_name":2541,"surname":2542},"Bulgaria)","Petya Osenova (Sofia University",{"given_name":2544,"surname":2545},"USA)","James Pustejovsky (Brandeis University",{"given_name":2535,"surname":2547},"Claus Zinn (Tübingen University",{"workshop_id":2549,"year":7,"full_workshop_id":2550,"proceedings_title":2551,"paperCount":469,"doi":2552,"pdf_url":2553,"venue_ids":2554,"publisher":13,"editors":2555,"conference_name":33,"conference_acronym":34,"conference_number":35,"conference_location":36,"conference_city":37,"conference_country":38,"conference_start_date":39,"conference_end_date":40},"soconnlpsi","lrec2026_ws_soconnlpsi","Proceedings of the 1st Workshop on Social Context (SoCon) and the 2nd Workshop on Integrating NLP and Psychology to Study Social Interactions (NLPSI) @ LREC 2026","10.63317\u002F5qbp9pb9xpfe","http:\u002F\u002Fwww.lrec-conf.org\u002Fproceedings\u002Flrec2026\u002Fworkshops\u002Fsoconnlpsi\u002F2026.soconnlpsi-1.0.pdf","soconnlpsi|ws",[2556,2558,2559,2562,2563,2566,2569,2572,2573,2574,2577,2578,2581,2584,2587],{"given_name":1433,"surname":2557},"Antonio Stranisci",{"given_name":1984,"surname":1985},{"given_name":2560,"surname":2561},"Sofie","Labat",{"given_name":2126,"surname":2127},{"given_name":2564,"surname":2565},"Aswathy","Velutharambath",{"given_name":2567,"surname":2568},"Sabine","Weber",{"given_name":2570,"surname":2571},"Rossana","Damiano",{"given_name":1536,"surname":1537},{"given_name":61,"surname":62},{"given_name":2575,"surname":2576},"Bennett","Kleinberg",{"given_name":474,"surname":475},{"given_name":2579,"surname":2580},"Viviana","Patti",{"given_name":2582,"surname":2583},"Flor","Miriam Plaza-del-Arco",{"given_name":2585,"surname":2586},"Maarten","Sap",{"given_name":2588,"surname":2589},"Seid","Muhie Yimam",{"workshop_id":2591,"year":7,"full_workshop_id":2592,"proceedings_title":2593,"paperCount":771,"doi":2594,"pdf_url":2595,"venue_ids":2596,"publisher":13,"editors":2597,"conference_name":33,"conference_acronym":34,"conference_number":35,"conference_location":36,"conference_city":37,"conference_country":38,"conference_start_date":39,"conference_end_date":40},"speakable","lrec2026_ws_speakable","Proceedings of Speech Language Models in Low-Resource Settings: Performance, Evaluation, and Bias Analysis (SPEAKABLE) @ LREC 2026","10.63317\u002F443zrkx8bhr6","http:\u002F\u002Fwww.lrec-conf.org\u002Fproceedings\u002Flrec2026\u002Fworkshops\u002Fspeakable\u002F2026.speakable-1.0.pdf","speakable|ws",[2598,2601,2604,2606,2609,2611],{"given_name":2599,"surname":2600},"Nina","Hosseini-Kivanani",{"given_name":2602,"surname":2603},"Alessio","Brutti",{"given_name":1433,"surname":2605},"Matassoni",{"given_name":2607,"surname":2608},"Sandipana","Dowerah",{"given_name":1530,"surname":2610},"Liga",{"given_name":2612,"surname":2613},"Christoph","Schommer",{"workshop_id":2615,"year":7,"full_workshop_id":2616,"proceedings_title":2617,"paperCount":2366,"doi":2618,"pdf_url":2619,"venue_ids":2620,"publisher":13,"editors":2621,"conference_name":33,"conference_acronym":34,"conference_number":35,"conference_location":36,"conference_city":37,"conference_country":38,"conference_start_date":39,"conference_end_date":40},"udw","lrec2026_ws_udw","Proceedings of the Ninth Workshop on Universal            Dependencies (UDW 2026)","10.63317\u002F4c2x4v6ohrvs","http:\u002F\u002Fwww.lrec-conf.org\u002Fproceedings\u002Flrec2026\u002Fworkshops\u002Fudw\u002F2026.udw-1.0.pdf","udw|ws",[],{"workshop_id":859,"year":7,"full_workshop_id":2623,"proceedings_title":2624,"paperCount":628,"doi":2625,"pdf_url":2626,"venue_ids":1821,"publisher":13,"editors":2627,"conference_name":33,"conference_acronym":34,"conference_number":35,"conference_location":36,"conference_city":37,"conference_country":38,"conference_start_date":39,"conference_end_date":40},"lrec2026_ws_wildre","Proceedings of the 8th Workshop on Indian Language Data: Resources and Evaluation","10.63317\u002F32ouujp5bxoa","http:\u002F\u002Fwww.lrec-conf.org\u002Fproceedings\u002Flrec2026\u002Fworkshops\u002Fwildre\u002F2026.wildre-1.0.pdf",[2628,2629,2630,2632],{"given_name":1824,"surname":1825},{"given_name":552,"surname":553},{"given_name":1827,"surname":2631},"L",{"given_name":2633,"surname":1783},"Devendr",{"conference_id":6,"year":7,"proceedings_title":8,"venue_ids":9,"isbn":10,"issn":11,"doi":12,"publisher":13,"editors":2635,"conference_name":33,"conference_acronym":34,"conference_number":35,"conference_location":36,"conference_city":37,"conference_country":38,"conference_start_date":39,"conference_end_date":40,"conference_url":41,"pdf_url":42,"img_conf_url":43,"paperCount":44},[2636,2637,2638,2639,2640,2641],{"given_name":16,"surname":17},{"given_name":19,"surname":20},{"given_name":22,"surname":23},{"given_name":25,"surname":26},{"given_name":28,"surname":29},{"given_name":31,"surname":32},{"workshop":2643,"papers":2650},{"workshop_id":1901,"year":7,"full_workshop_id":1902,"proceedings_title":1903,"paperCount":1904,"doi":1905,"pdf_url":1906,"venue_ids":1907,"publisher":13,"editors":2644,"conference_name":33,"conference_acronym":34,"conference_number":35,"conference_location":36,"conference_city":37,"conference_country":38,"conference_start_date":39,"conference_end_date":40},[2645,2646,2647,2648,2649],{"given_name":1910,"surname":1911},{"given_name":1913,"surname":1914},{"given_name":1916,"surname":1917},{"given_name":1919,"surname":1920},{"given_name":1922,"surname":1923},[2651,2665,2690,2713,2752,2769,2789,2818,2834,2853,2867,2894,2918,2950,2970,2987,3024,3041,3063,3083,3099,3115,3129,3155,3181,3200,3226,3243,3257,3271,3291,3314,3336,3367,3387,3423,3444,3476,3505,3522,3544],{"paper_id":2652,"title":2653,"year":7,"month":358,"day":135,"doi":2654,"resource_url":2655,"first_page":459,"last_page":193,"pdf_url":2656,"poster_url":135,"slide_url":135,"video_url":135,"supplementary_url":135,"bibkey":2657,"paper_type":2658,"authors":2659,"abstract":2664},"lrec2026-ws-clinicalnlp-01","Overview of the MEDIQA-EVAL 2026 Shared Task on Evaluation Metrics in Medical Multimodal Question Answering ","10.63317\u002F3rkabnqpbd84","https:\u002F\u002Flrec.elra.info\u002Flrec2026-ws-clinicalnlp-01","http:\u002F\u002Fwww.lrec-conf.org\u002Fproceedings\u002Flrec2026\u002Fworkshops\u002Fclinicalnlp\u002Fpdf\u002F2026.clinicalnlp-1.1.pdf","benabacha-etal-2026-overview","workshop",[2660,2661],{"paper_id":2652,"author_seq":459,"given_name":1910,"surname":1911,"affiliation":135,"orcid":135},{"paper_id":2652,"author_seq":434,"given_name":2662,"surname":2663,"affiliation":135,"orcid":135},"Wen-wai","Yim","Evaluating clinical text generation remains challenging, as automatic metrics often correlate weakly with clinician judgments. This issue is particularly pronounced in medical multimodal question answering (MMQA), where systems must integrate visual and textual information and evaluation must capture factual accuracy, visual grounding, completeness, and overall coherence. Despite rapid progress in MMQA, there is limited consensus on clinically meaningful evaluation, and existing metrics, largely adapted from general NLG or VQA, often fail to capture domain-specific criteria. We introduce MEDIQA-EVAL 2026, a shared task on evaluation metrics for medical multimodal QA. To our knowledge, this is the first shared task focused on evaluating automatic metrics in this setting. We release a dataset of medical visual question-answer pairs annotated with multidimensional clinician judgments. Systems are evaluated by the correlation of their metric scores with expert ratings on a held-out test set. Participants explored diverse approaches, including vision-language models, retrieval-augmented judging, metric-specific classifiers, reinforcement learning, and LLM-as-a-judge frameworks. Results show that model-based evaluators achieve stronger alignment with human judgments than traditional NLG metrics, particularly on English data, while performance remains lower on Chinese, highlighting challenges in multilingual evaluation. Notably, our MEDIQA LLM-as-a-judge approach achieves strong performance across both languages.",{"paper_id":2666,"title":2667,"year":7,"month":358,"day":135,"doi":2668,"resource_url":2669,"first_page":161,"last_page":2670,"pdf_url":2671,"poster_url":135,"slide_url":135,"video_url":135,"supplementary_url":135,"bibkey":2672,"paper_type":2658,"authors":2673,"abstract":2689},"lrec2026-ws-clinicalnlp-02","SUAT-BMI at MEDIQA-EVAL 2026: An Ensemble Approach to Language Models as Judges for Automatic Rating of Medical Responses ","10.63317\u002F2kdt525sk8is","https:\u002F\u002Flrec.elra.info\u002Flrec2026-ws-clinicalnlp-02","18","http:\u002F\u002Fwww.lrec-conf.org\u002Fproceedings\u002Flrec2026\u002Fworkshops\u002Fclinicalnlp\u002Fpdf\u002F2026.clinicalnlp-1.2.pdf","peng-etal-2026-suat",[2674,2677,2680,2683,2685,2688],{"paper_id":2666,"author_seq":459,"given_name":2675,"surname":2676,"affiliation":135,"orcid":135},"Xinzhe","Peng",{"paper_id":2666,"author_seq":434,"given_name":2678,"surname":2679,"affiliation":135,"orcid":135},"Liyuan","E",{"paper_id":2666,"author_seq":408,"given_name":2681,"surname":2682,"affiliation":135,"orcid":135},"Kun","Feng",{"paper_id":2666,"author_seq":387,"given_name":2684,"surname":1290,"affiliation":135,"orcid":135},"Jielin",{"paper_id":2666,"author_seq":358,"given_name":2686,"surname":2687,"affiliation":135,"orcid":135},"Yuxuan","Tang",{"paper_id":2666,"author_seq":333,"given_name":2032,"surname":1290,"affiliation":135,"orcid":135},"The MEDIQA-EVAL 2026 shared task focuses on developing automatic evaluation metrics for LLM-generated responses in dermatology and wound care. While LLMs have shown promise as judge models, the reliability of these metrics remains underexplored. In this work, we study how well judge models can approximate human expert ratings across clinical evaluation criteria. We evaluate multiple approaches, including few-shot prompting, BERT fine-tuning, and retrieval-augmented generation (RAG), and combine them in an ensemble framework. Our method achieves a correlation score of 0.481, ranking first among 41 participating teams. Our results provide insight into the reliability of LLM-based evaluation metrics and highlight their potential for scalable clinical assessment.",{"paper_id":2691,"title":2692,"year":7,"month":358,"day":135,"doi":2693,"resource_url":2694,"first_page":2695,"last_page":2696,"pdf_url":2697,"poster_url":135,"slide_url":135,"video_url":135,"supplementary_url":135,"bibkey":2698,"paper_type":2658,"authors":2699,"abstract":2712},"lrec2026-ws-clinicalnlp-03","Overview of the MEDIQA-SYNUR 2026 Shared Task on Observation Extraction from Nurse Dictations ","10.63317\u002F3s6vwtvsw85q","https:\u002F\u002Flrec.elra.info\u002Flrec2026-ws-clinicalnlp-03","19","26","http:\u002F\u002Fwww.lrec-conf.org\u002Fproceedings\u002Flrec2026\u002Fworkshops\u002Fclinicalnlp\u002Fpdf\u002F2026.clinicalnlp-1.3.pdf","michalopoulos-etal-2026-overview",[2700,2702,2705,2708,2711],{"paper_id":2691,"author_seq":459,"given_name":424,"surname":2701,"affiliation":135,"orcid":135},"Michalopoulos",{"paper_id":2691,"author_seq":434,"given_name":2703,"surname":2704,"affiliation":135,"orcid":135},"Jean-Philippe","Corbeil",{"paper_id":2691,"author_seq":408,"given_name":2706,"surname":2707,"affiliation":135,"orcid":135},"Cari","Bader",{"paper_id":2691,"author_seq":387,"given_name":2709,"surname":2710,"affiliation":135,"orcid":135},"Nathan","Bodenstab",{"paper_id":2691,"author_seq":358,"given_name":1910,"surname":1911,"affiliation":135,"orcid":135},"Hospital nurses spend a significant portion of their shifts performing manual data entry tasks. An automatic solution for extracting medical information from nurse dictations into large spreadsheet ontology (flowsheet) could reduce the documentation burden of nurses and alleviate nurse burnout. We introduce the MEDIQA-SYNUR shared task, the first challenge on extracting and normalizing clinical observations from nurse dictations and mapping them to a large ontology of clinical concepts. 13 teams participated in the challenge and experimented with a broad range of approaches. In this paper, we describe the MEDIQA-SYNUR task, the datasets, and the participant’s results and solutions.",{"paper_id":2714,"title":2715,"year":7,"month":358,"day":135,"doi":2716,"resource_url":2717,"first_page":2718,"last_page":2719,"pdf_url":2720,"poster_url":135,"slide_url":135,"video_url":135,"supplementary_url":135,"bibkey":2721,"paper_type":2658,"authors":2722,"abstract":2751},"lrec2026-ws-clinicalnlp-04","SemAnTICA Lab at MediQA-SYNUR 2026: Route, Extract and Verify – An LLM-gated Ensemble for Parsing Nurse Dictations ","10.63317\u002F3cfpmqqpmdq5","https:\u002F\u002Flrec.elra.info\u002Flrec2026-ws-clinicalnlp-04","27","37","http:\u002F\u002Fwww.lrec-conf.org\u002Fproceedings\u002Flrec2026\u002Fworkshops\u002Fclinicalnlp\u002Fpdf\u002F2026.clinicalnlp-1.4.pdf","hwang-etal-2026-semantica",[2723,2726,2729,2732,2734,2737,2740,2743,2745,2748],{"paper_id":2714,"author_seq":459,"given_name":2724,"surname":2725,"affiliation":135,"orcid":135},"Sy","Hwang",{"paper_id":2714,"author_seq":434,"given_name":2727,"surname":2728,"affiliation":135,"orcid":135},"Katherine S.","Pitcher",{"paper_id":2714,"author_seq":408,"given_name":2730,"surname":2731,"affiliation":135,"orcid":135},"Sue Hyon","Kim",{"paper_id":2714,"author_seq":387,"given_name":2733,"surname":1379,"affiliation":135,"orcid":135},"Yoonjae",{"paper_id":2714,"author_seq":358,"given_name":2735,"surname":2736,"affiliation":135,"orcid":135},"Hayoung K.","Donelly",{"paper_id":2714,"author_seq":333,"given_name":2738,"surname":2739,"affiliation":135,"orcid":135},"Harsh","Bandhey",{"paper_id":2714,"author_seq":309,"given_name":2741,"surname":2742,"affiliation":135,"orcid":135},"Andrew J.","King",{"paper_id":2714,"author_seq":280,"given_name":1315,"surname":2744,"affiliation":135,"orcid":135},"O'Connor",{"paper_id":2714,"author_seq":252,"given_name":2746,"surname":2747,"affiliation":135,"orcid":135},"Ryan J.","Urbanowicz",{"paper_id":2714,"author_seq":224,"given_name":2749,"surname":2750,"affiliation":135,"orcid":135},"Danielle L.","Mowery","We describe the Semantic Analysis of Text to Inform Clinical Action (SemAnTICA) Lab’s system for the MediQA-SYNUR 2026 shared task on extracting structured clinical observations from nurse dictation transcripts. The task requires mapping observations from disfluent conversational text to a large, fixed ontology and producing strictly normalized outputs, where small amounts of concept over-selection severely degrade micro-F1 score. Our approach evolved from a full-schema in-context baseline to a pipeline that explicitly separates concept selection from value extraction. We first preprocess transcripts, then generate transcript-specific concept candidates using hybrid sparse–dense retrieval. The candidates are then pruned with an evidence-based filter. For extraction, we adopt a system-level mixture-of-experts design with an online LLM router that selects a subset of domain-specialized experts per transcript. Each expert operates over a constrained schema partition to reduce spurious predictions. We enhance robustness with agreement-gated ensembling and targeted adjudication for ambiguous cases. Finally, we intersect complementary high-recall and high-precision runs to produce the best submission. Our system ranked first on the official test leaderboard with F1 = 0.814, P = 0.826, R = 0.801.",{"paper_id":2753,"title":2754,"year":7,"month":358,"day":135,"doi":2755,"resource_url":2756,"first_page":2757,"last_page":2758,"pdf_url":2759,"poster_url":135,"slide_url":135,"video_url":135,"supplementary_url":135,"bibkey":2760,"paper_type":2658,"authors":2761,"abstract":2768},"lrec2026-ws-clinicalnlp-05","L2D-Clinical: Learning to Defer for Adaptive Model Selection in Clinical Text Classification ","10.63317\u002F2jex98nw9bmz","https:\u002F\u002Flrec.elra.info\u002Flrec2026-ws-clinicalnlp-05","38","48","http:\u002F\u002Fwww.lrec-conf.org\u002Fproceedings\u002Flrec2026\u002Fworkshops\u002Fclinicalnlp\u002Fpdf\u002F2026.clinicalnlp-1.5.pdf","kondadadi-etal-2026-l2d",[2762,2765],{"paper_id":2753,"author_seq":459,"given_name":2763,"surname":2764,"affiliation":135,"orcid":135},"Rishik","Kondadadi",{"paper_id":2753,"author_seq":434,"given_name":2766,"surname":2767,"affiliation":135,"orcid":135},"John E.","Ortega","Clinical text classification requires choosing between specialized fine-tuned models (BERT variants) and general-purpose large language models (LLMs), yet neither dominates across all instances. We introduce Learning to Defer for clinical text (L2D-Clinical), a framework that learns when a BERT classifier should defer to an LLM based on uncertainty signals and text characteristics. Unlike prior L2D work that defers to human experts assumed universally superior, our approach enables adaptive deferral-improving accuracy when the LLM complements BERT. We evaluate on two clinical tasks: (1) ADE detection (ADE Corpus V2), where BioBERT (F1=0.911) outperforms the LLM (F1=0.765), and (2) treatment outcome classification (MIMIC-IV with multi-LLM consensus ground truth), where GPT-5-nano (F1=0.967) outperforms ClinicalBERT (F1=0.887). On ADE, L2D-Clinical achieves F1=0.928 (+1.7 points over BERT) by selectively deferring 7% of instances where the LLM’s high recall compensates for BERT’s misses. On MIMIC, L2D-Clinical achieves F1=0.980 (+9.3 points over BERT) by deferring only 16.8% of cases to the LLM. The key insight is that L2D-Clinical learns to selectively leverage LLM strengths while minimizing API costs.",{"paper_id":2770,"title":2771,"year":7,"month":358,"day":135,"doi":2772,"resource_url":2773,"first_page":2774,"last_page":2775,"pdf_url":2776,"poster_url":135,"slide_url":135,"video_url":135,"supplementary_url":135,"bibkey":2777,"paper_type":2658,"authors":2778,"abstract":2788},"lrec2026-ws-clinicalnlp-06","TRUMEDIQA: A Modular Trustworthy RAG Pipeline for Multilingual Medical Question Answering ","10.63317\u002F4emkg4hncbzi","https:\u002F\u002Flrec.elra.info\u002Flrec2026-ws-clinicalnlp-06","49","56","http:\u002F\u002Fwww.lrec-conf.org\u002Fproceedings\u002Flrec2026\u002Fworkshops\u002Fclinicalnlp\u002Fpdf\u002F2026.clinicalnlp-1.6.pdf","elfatimi-etal-2026-trumediqa",[2779,2782,2785],{"paper_id":2770,"author_seq":459,"given_name":2780,"surname":2781,"affiliation":135,"orcid":135},"Meryem","El Fatimi",{"paper_id":2770,"author_seq":434,"given_name":2783,"surname":2784,"affiliation":135,"orcid":135},"Ayoub","Nainia",{"paper_id":2770,"author_seq":408,"given_name":2786,"surname":2787,"affiliation":135,"orcid":135},"Jihad","Zahir","Medical question answering systems must balance usefulness with safety, particularly in low-resource linguistic settings where robustness is limited and hallucinations can cause harm. We present TRUMEDIQA, a reproducible multilingual medical QA pipeline for Moroccan Darija, Arabic, French, and English, deployed on WhatsApp with text and voice interactions. TRUMEDIQA uses layered decision-making: (i) language identification, (ii) a pre-retrieval intent router that maps queries to one of 38 clinical FAQ categories to constrain retrieval, and (iii) post-retrieval LLM-based re-ranking that selects the best candidate answer or returns a null decision to trigger a safe fallback (abstention). Answers are retrieved from a curated FAQ knowledge base validated by medical professionals. We evaluate TRUMEDIQA with 21 participants submitting 290 questions across four languages. An expert annotator labels each interaction as relevant, acceptable, or irrelevant, and we also measure correct abstentions when no suitable answer exists in the knowledge base. An ablation study shows that routing and re-ranking improve the weighted relevance score from 0.25 to 0.94 and precision from 0.53 to 0.98 versus a naïve retrieval baseline, while increasing correct abstention on unanswerable queries from 4.38% to 69.77%.",{"paper_id":2790,"title":2791,"year":7,"month":358,"day":135,"doi":2792,"resource_url":2793,"first_page":2794,"last_page":2795,"pdf_url":2796,"poster_url":135,"slide_url":135,"video_url":135,"supplementary_url":135,"bibkey":2797,"paper_type":2658,"authors":2798,"abstract":2817},"lrec2026-ws-clinicalnlp-07","Evaluating the Retrieval Component in a Retrieval-Augmented Summarization System for Patient Records in French ","10.63317\u002F4cy8xxinjw7z","https:\u002F\u002Flrec.elra.info\u002Flrec2026-ws-clinicalnlp-07","57","65","http:\u002F\u002Fwww.lrec-conf.org\u002Fproceedings\u002Flrec2026\u002Fworkshops\u002Fclinicalnlp\u002Fpdf\u002F2026.clinicalnlp-1.7.pdf","naguib-etal-2026-evaluating",[2799,2801,2804,2807,2810,2811,2814],{"paper_id":2790,"author_seq":459,"given_name":1433,"surname":2800,"affiliation":135,"orcid":135},"Naguib",{"paper_id":2790,"author_seq":434,"given_name":2802,"surname":2803,"affiliation":135,"orcid":135},"Christel","Gérardin",{"paper_id":2790,"author_seq":408,"given_name":2805,"surname":2806,"affiliation":135,"orcid":135},"Victor","Beaucoté",{"paper_id":2790,"author_seq":387,"given_name":2808,"surname":2809,"affiliation":135,"orcid":135},"Cyril","Charron",{"paper_id":2790,"author_seq":358,"given_name":611,"surname":116,"affiliation":135,"orcid":135},{"paper_id":2790,"author_seq":333,"given_name":2812,"surname":2813,"affiliation":135,"orcid":135},"Aurélie","Névéol",{"paper_id":2790,"author_seq":309,"given_name":2815,"surname":2816,"affiliation":135,"orcid":135},"Xavier","Tannier","In emergency and intensive care settings, clinicians must process large volumes of patient data to make time-sensitive decisions. Summarizing patient records can help reduce cognitive load and improve decision-making, but the complexity and variability of clinical documentation create challenges. This study explores a Retrieval-Augmented Generation (RAG) approach, consisting of two phases: (1) retrieval of relevant clinical information, and (2) generation of a summary. This paper evaluates the retrieval component of RAG systems, focusing on its performance in clinical contexts. Using French clinical text, we assess retrieval models and propose an annotation-based querying method to improve accuracy and consistency in retrieving core clinical information. We use an annotated dataset from anonymized-hospital to benchmark retrieval models tailored for French clinical records. The proposed annotation-based querying method is compared to traditional prompt-based approaches, demonstrating improved retrieval performance. The findings indicate that specialized retrieval techniques enhance the effectiveness of RAG systems in clinical settings, providing more accurate and relevant information for summarization. The study contributes to the development of clinical decision support tools by improving the retrieval process in RAG systems. The proposed methods offer a structured approach to summarizing patient records, which may help clinicians manage information more efficiently.",{"paper_id":2819,"title":2820,"year":7,"month":358,"day":135,"doi":2821,"resource_url":2822,"first_page":2823,"last_page":2824,"pdf_url":2825,"poster_url":135,"slide_url":135,"video_url":135,"supplementary_url":135,"bibkey":2826,"paper_type":2658,"authors":2827,"abstract":2833},"lrec2026-ws-clinicalnlp-08","Retrieval-Augmented Generation Based Nurse Observation Extraction ","10.63317\u002F2hexmrrsvigr","https:\u002F\u002Flrec.elra.info\u002Flrec2026-ws-clinicalnlp-08","66","72","http:\u002F\u002Fwww.lrec-conf.org\u002Fproceedings\u002Flrec2026\u002Fworkshops\u002Fclinicalnlp\u002Fpdf\u002F2026.clinicalnlp-1.8.pdf","hwang-etal-2026-retrieval",[2828,2830],{"paper_id":2819,"author_seq":459,"given_name":2829,"surname":2725,"affiliation":135,"orcid":135},"Kyomin",{"paper_id":2819,"author_seq":434,"given_name":2831,"surname":2832,"affiliation":135,"orcid":135},"Nojun","Kwak","Recent advancements in Large Language Models (LLMs) have played a significant role in reducing human workload across various domains, a trend that is increasingly extending into the medical field. In this paper, we propose an automated pipeline designed to alleviate the burden on nurses by automatically extracting clinical observations from nurse dictations. To ensure accurate extraction, we introduce a method based on Retrieval-Augmented Generation (RAG). Our approach demonstrates effective performance, achieving an F1-score of 0.796 on the MEDIQA-SYNUR test dataset.",{"paper_id":2835,"title":2836,"year":7,"month":358,"day":135,"doi":2837,"resource_url":2838,"first_page":2839,"last_page":2840,"pdf_url":2841,"poster_url":135,"slide_url":135,"video_url":135,"supplementary_url":135,"bibkey":2842,"paper_type":2658,"authors":2843,"abstract":2852},"lrec2026-ws-clinicalnlp-09","Automatic Generation of Discharge Summaries Using Large Language Models: A Systematic Literature Review ","10.63317\u002F5dnbkz4d44kz","https:\u002F\u002Flrec.elra.info\u002Flrec2026-ws-clinicalnlp-09","73","82","http:\u002F\u002Fwww.lrec-conf.org\u002Fproceedings\u002Flrec2026\u002Fworkshops\u002Fclinicalnlp\u002Fpdf\u002F2026.clinicalnlp-1.9.pdf","molinopiar-etal-2026-automatic",[2844,2846,2849],{"paper_id":2835,"author_seq":459,"given_name":1146,"surname":2845,"affiliation":135,"orcid":135},"Molino-Piñar",{"paper_id":2835,"author_seq":434,"given_name":2847,"surname":2848,"affiliation":135,"orcid":135},"Manuel Carlos","Diaz Galiano",{"paper_id":2835,"author_seq":408,"given_name":2850,"surname":2851,"affiliation":135,"orcid":135},"María-Teresa","Martín-Valdivia","Discharge summaries are critical documents for continuity of care, yet their manual creation imposes significant burdens on clinical staff. This systematic literature review examines current approaches to automatic generation of discharge summaries using Natural Language Processing (NLP) and Large Language Models (LLMs). Following the Kitchenham guidelines for systematic reviews in software engineering, we searched Scopus and PubMed databases for studies published between 2023 and 2026, identifying 9 primary studies from an initial pool of 102 papers. Our analysis reveals that GPT-4 and its variants dominate current research (appearing in 6 of 9 studies), while open-source alternatives like LLaMA show promise for privacy-preserving deployments. Evaluation primarily relies on automatic metrics (ROUGE, BLEU) combined with human expert assessment. Key challenges include hallucination rates ranging from 33% to 64%, information omission, integration with Electronic Health Record (EHR) systems, and context window limitations. Studies addressing factuality employ human-in-the-loop validation, prompt engineering techniques, and knowledge graph-based correction mechanisms. Despite these challenges, recent implementations demonstrate clinical feasibility, with one study achieving a 94.35% System Usability Score. This review provides a comprehensive synthesis of the state-of-the-art and identifies opportunities for future research in this rapidly evolving field.",{"paper_id":2854,"title":2855,"year":7,"month":358,"day":135,"doi":2856,"resource_url":2857,"first_page":2858,"last_page":2859,"pdf_url":2860,"poster_url":135,"slide_url":135,"video_url":135,"supplementary_url":135,"bibkey":2861,"paper_type":2658,"authors":2862,"abstract":2866},"lrec2026-ws-clinicalnlp-10","Smart_solutions at MEDIQA-SYNUR 2026: A Multi-Stage LLM Pipeline for Nursing Observation Extraction ","10.63317\u002F27y4nwpv3xyg","https:\u002F\u002Flrec.elra.info\u002Flrec2026-ws-clinicalnlp-10","83","91","http:\u002F\u002Fwww.lrec-conf.org\u002Fproceedings\u002Flrec2026\u002Fworkshops\u002Fclinicalnlp\u002Fpdf\u002F2026.clinicalnlp-1.10.pdf","munjal-2026-smart_solutions",[2863],{"paper_id":2854,"author_seq":459,"given_name":2864,"surname":2865,"affiliation":135,"orcid":135},"Prateek","Munjal","Extracting clinical observations from nursing dictations addresses an important problem of addressing burden in clinical documentation. In this work, we describe our approach submitted to MEDIQA-SYNUR 2026, which achieved third place among participating teams with a balanced precision and recall of 0.80 on the unseen test set. Our approach, instead of finetuning LLMs, is to adopt a multi-stage pipeline of agents: Observation Agent, Ontology Matching Agent, Relevance Scoring Agent, Evidence Assignment Agent, and Formatting Agent. First, the Observation Agent extracts clinical observations and corresponding evidence from the nurse transcript. These observations are then processed by the Ontology Matching Agent, which maps them to a restricted set of candidate ontology fields via TF-IDF–based retrieval, and subsequently evaluated by the Relevance Scoring Agent, which assigns continuous support scores (1–5) to each candidate field. Finally, field value assignments are performed by the Evidence-Based Agent, which extracts values strictly from nurse transcripts and clinical observations (Observation Agent outputs) to populate each ontology field. These outputs are then formatted by the Formatting Agent to ensure correct submission structure with the necessary metadata. Our agentic system results suggest that combination of agents with prompt engineering can narrow the gap between general and specialized clinical NLP models, making it an immediately deployable alternative to traditional fine-tuning.",{"paper_id":2868,"title":2869,"year":7,"month":358,"day":135,"doi":2870,"resource_url":2871,"first_page":2872,"last_page":2873,"pdf_url":2874,"poster_url":135,"slide_url":135,"video_url":135,"supplementary_url":135,"bibkey":2875,"paper_type":2658,"authors":2876,"abstract":2893},"lrec2026-ws-clinicalnlp-11","GS-BrainText: A Multi-Site Brain Imaging Report Dataset from Generation Scotland for Clinical Natural Language Processing Development and Validation ","10.63317\u002F45m3apdidcpj","https:\u002F\u002Flrec.elra.info\u002Flrec2026-ws-clinicalnlp-11","92","102","http:\u002F\u002Fwww.lrec-conf.org\u002Fproceedings\u002Flrec2026\u002Fworkshops\u002Fclinicalnlp\u002Fpdf\u002F2026.clinicalnlp-1.11.pdf","alex-etal-2026-gs",[2877,2880,2882,2885,2887,2890],{"paper_id":2868,"author_seq":459,"given_name":2878,"surname":2879,"affiliation":135,"orcid":135},"Beatrice","Alex",{"paper_id":2868,"author_seq":434,"given_name":1205,"surname":2881,"affiliation":135,"orcid":135},"Grover",{"paper_id":2868,"author_seq":408,"given_name":2883,"surname":2884,"affiliation":135,"orcid":135},"Arlene","Casey",{"paper_id":2868,"author_seq":387,"given_name":598,"surname":2886,"affiliation":135,"orcid":135},"Tobin",{"paper_id":2868,"author_seq":358,"given_name":2888,"surname":2889,"affiliation":135,"orcid":135},"Heather","Whalley",{"paper_id":2868,"author_seq":333,"given_name":2891,"surname":2892,"affiliation":135,"orcid":135},"William","Whiteley","We present GS-BrainText, a curated dataset of 8,511 brain radiology reports from the Generation Scotland cohort, of which 2,431 are annotated for 24 brain disease phenotypes. This multi-site dataset spans five Scottish NHS health boards and includes broad age representation (mean age 58, median age 53), making it uniquely valuable for developing and evaluating generalisable clinical natural language processing (NLP) algorithms and tools. Expert annotations were performed by a multidisciplinary clinical team using an annotation schema, with 10–100% double annotation per NHS health board and rigorous quality assurance. Benchmark evaluation using EdIE-R, an existing rule-based NLP system developed in conjunction with the annotation schema, revealed some performance variation across health boards (F1: 86.13-98.13), phenotypes (F1: 22.22-100) and age groups (F1: 87.01-98.13), highlighting critical challenges in generalisation of NLP tools. The GS-BrainText dataset addresses a significant gap in available UK clinical text resources and provides a valuable resource for the study of linguistic variation, diagnostic uncertainty expression and the impact of data characteristics on NLP system performance.",{"paper_id":2895,"title":2896,"year":7,"month":358,"day":135,"doi":2897,"resource_url":2898,"first_page":2899,"last_page":2900,"pdf_url":2901,"poster_url":135,"slide_url":135,"video_url":135,"supplementary_url":135,"bibkey":2902,"paper_type":2658,"authors":2903,"abstract":2917},"lrec2026-ws-clinicalnlp-12","SASTA Self Assessment: An efficient human-in-the-loop strategy for developmental and pathological language analysis ","10.63317\u002F22ko26n2nfwv","https:\u002F\u002Flrec.elra.info\u002Flrec2026-ws-clinicalnlp-12","103","112","http:\u002F\u002Fwww.lrec-conf.org\u002Fproceedings\u002Flrec2026\u002Fworkshops\u002Fclinicalnlp\u002Fpdf\u002F2026.clinicalnlp-1.12.pdf","odijk-etal-2026-sasta",[2904,2905,2908,2911,2914],{"paper_id":2895,"author_seq":459,"given_name":122,"surname":123,"affiliation":135,"orcid":135},{"paper_id":2895,"author_seq":434,"given_name":2906,"surname":2907,"affiliation":135,"orcid":135},"Jelte","van Boheemen",{"paper_id":2895,"author_seq":408,"given_name":2909,"surname":2910,"affiliation":135,"orcid":135},"Xander","Vertegaal",{"paper_id":2895,"author_seq":387,"given_name":2912,"surname":2913,"affiliation":135,"orcid":135},"Tessel","Boerma",{"paper_id":2895,"author_seq":358,"given_name":2915,"surname":2916,"affiliation":135,"orcid":135},"Marijn","Schraagen","This paper introduces SASTA self-assessment, i.e., a self-assessment procedure for the SASTA application for semiautomatic analysis of spontaneous language transcripts for Dutch. We introduce SASTA and the methods that it supports. These methods are used to assess the language development of young children and to assess the language skills of patients with aphasia. We illustrate this with typical example utterances. The performance of SASTA is good but not good enough for fully automatic use. The self-assessment procedure attempts to automatically identify utterances that require revision by a human expert. The self-assessment procedure gives promising results for datasets for the ASTA and STAP methods. Significant improvements are still needed for the TARSP method, but there is still potential for such improvements, and we sketch some directions to achieve such improvements.",{"paper_id":2919,"title":2920,"year":7,"month":358,"day":135,"doi":2921,"resource_url":2922,"first_page":2923,"last_page":2924,"pdf_url":2925,"poster_url":135,"slide_url":135,"video_url":135,"supplementary_url":135,"bibkey":2926,"paper_type":2658,"authors":2927,"abstract":2949},"lrec2026-ws-clinicalnlp-13","Differentially Private De-identification of Dutch Clinical Notes: A Comparative Evaluation ","10.63317\u002F2xrg7fmndfhy","https:\u002F\u002Flrec.elra.info\u002Flrec2026-ws-clinicalnlp-13","113","124","http:\u002F\u002Fwww.lrec-conf.org\u002Fproceedings\u002Flrec2026\u002Fworkshops\u002Fclinicalnlp\u002Fpdf\u002F2026.clinicalnlp-1.13.pdf","miranda-etal-2026-differentially",[2928,2931,2934,2937,2940,2943,2946],{"paper_id":2919,"author_seq":459,"given_name":2929,"surname":2930,"affiliation":135,"orcid":135},"Michele","Miranda",{"paper_id":2919,"author_seq":434,"given_name":2932,"surname":2933,"affiliation":135,"orcid":135},"Xinlan","Yan",{"paper_id":2919,"author_seq":408,"given_name":2935,"surname":2936,"affiliation":135,"orcid":135},"Nishant","Mishra",{"paper_id":2919,"author_seq":387,"given_name":2938,"surname":2939,"affiliation":135,"orcid":135},"Rachel","Murphy",{"paper_id":2919,"author_seq":358,"given_name":2941,"surname":2942,"affiliation":135,"orcid":135},"Ameen Abu","Hanna",{"paper_id":2919,"author_seq":333,"given_name":2944,"surname":2945,"affiliation":135,"orcid":135},"Sébastien","Bratières",{"paper_id":2919,"author_seq":309,"given_name":2947,"surname":2948,"affiliation":135,"orcid":135},"Iacer","Calixto","Protecting patient privacy in clinical narratives is essential for enabling secondary use of healthcare data under regulations such as GDPR and HIPAA. While manual de-identification remains the gold standard, it is costly and slow, motivating the need for automated methods that combine privacy guarantees with high utility. Historically, most automated text de-identification pipelines employed named entity recognition (NER) to identify protected entities for redaction. Although methods based on differential privacy (DP) provide formal privacy guarantees, more recently also large language models (LLMs) are increasingly used for text de-identification in the clinical domain. In this work, we present the first comparative study of DP, NER, and LLMs for Dutch clinical text de-identification. We investigate these methods separately as well as hybrid strategies that apply NER or LLM preprocessing prior to DP, and assess performance in terms of privacy leakage and extrinsic evaluation (entity and relation classification). We show that DP mechanisms alone degrade utility substantially, but combining them with linguistic preprocessing, especially LLM-based redaction, significantly improves the privacy–utility trade-off.",{"paper_id":2951,"title":2952,"year":7,"month":358,"day":135,"doi":2953,"resource_url":2954,"first_page":2955,"last_page":2956,"pdf_url":2957,"poster_url":135,"slide_url":135,"video_url":135,"supplementary_url":135,"bibkey":2958,"paper_type":2658,"authors":2959,"abstract":2969},"lrec2026-ws-clinicalnlp-14","SQUCS at MEDIQA-SYNUR 2026: A Multi-Agent Open Source LLM System for Nursing Observation Extraction ","10.63317\u002F3cuv6m9wb5pm","https:\u002F\u002Flrec.elra.info\u002Flrec2026-ws-clinicalnlp-14","125","135","http:\u002F\u002Fwww.lrec-conf.org\u002Fproceedings\u002Flrec2026\u002Fworkshops\u002Fclinicalnlp\u002Fpdf\u002F2026.clinicalnlp-1.14.pdf","jeeballah-etal-2026-squcs",[2960,2963,2966],{"paper_id":2951,"author_seq":459,"given_name":2961,"surname":2962,"affiliation":135,"orcid":135},"Riham","JeebAllah",{"paper_id":2951,"author_seq":434,"given_name":2964,"surname":2965,"affiliation":135,"orcid":135},"Adhari","AlZaabi",{"paper_id":2951,"author_seq":408,"given_name":2967,"surname":2968,"affiliation":135,"orcid":135},"Abdulrahman Khalifa","AAlAbdulsalam","Clinical nursing documentation contains detailed observational information that is essential for patient monitoring and clinical decision-making, yet this information is predominantly recorded in free-text form. The MEDIQA-SYNUR shared task addresses this challenge by requiring systems to extract structured nursing observations from clinical transcripts under strict constraints on evidence grounding and value normalization. In this work, we present a multi-agent large language model (LLM)–based system for the MEDIQA-SYNUR task. We utilize the Llama3 open source LLM for this purpose for ease of local deployment within hospital digital infrastructure. Our system decomposes the extraction process into specialized agents responsible for schema-guided extraction, rule-based validation, and precision-oriented filtering. Starting from a baseline multi-agent pipeline, we conduct a systematic error analysis over the entire development set, examining all false positive and false negative predictions. Our final configuration, selected after extensive exploration and error analysis, combined transcript segmentation, the precision agent, and a suppression table derived from development-set analysis. On the development set, this setup achieved an F1 score of 0.6930 (precision = 0.6427, recall = 0.7518). Applying the same configuration directly to the test set, without any additional tuning, yielded an F1 score of 0.5923 (precision = 0.5292, recall = 0.6725). These results represent the most effective balance of precision and recall achieved through our iterative refinements and reflect the final state of the system as submitted for the competition",{"paper_id":2971,"title":2972,"year":7,"month":358,"day":135,"doi":2973,"resource_url":2974,"first_page":2975,"last_page":2976,"pdf_url":2977,"poster_url":135,"slide_url":135,"video_url":135,"supplementary_url":135,"bibkey":2978,"paper_type":2658,"authors":2979,"abstract":2986},"lrec2026-ws-clinicalnlp-15","LTRC-IIIT at MEDIQA-SYNUR 2026: Benchmarking a Fully Local, Training-Free RAG Pipeline ","10.63317\u002F3wbuccufqusz","https:\u002F\u002Flrec.elra.info\u002Flrec2026-ws-clinicalnlp-15","136","140","http:\u002F\u002Fwww.lrec-conf.org\u002Fproceedings\u002Flrec2026\u002Fworkshops\u002Fclinicalnlp\u002Fpdf\u002F2026.clinicalnlp-1.15.pdf","vaish-etal-2026-ltrc",[2980,2983],{"paper_id":2971,"author_seq":459,"given_name":2981,"surname":2982,"affiliation":135,"orcid":135},"Aashwin","Vaish",{"paper_id":2971,"author_seq":434,"given_name":2984,"surname":2985,"affiliation":135,"orcid":135},"Dipti Misra","Sharma","In this paper we present our solution to MEDIQA-SYNUR 2026 shared task organized at LREC-ClinicalNLP workshop. The goal of the task is to populate Electronic Health Record (EHR) flowsheets using the transcriptions of nurse dictations, to alleviate the extensive manual labor associated with sifting through large flowsheets of clinical concepts. We propose a modular architecture combining heuristic-driven Retrieval-Augmented Generation (RAG) with grammar-constrained decoding on an open-weight, quantized, 8B-parameter model (Llama 3.1 Instruct). Our system achieves an F1 score of 0.57, significantly trailing the initial zero-shot experiments with GPT-4o and placing it towards the lower end of the current leaderboard. We conduct a failure analysis of this approach while establishing a baseline for privacy-preserving, zero-shot documentation assistants.",{"paper_id":2988,"title":2989,"year":7,"month":358,"day":135,"doi":2990,"resource_url":2991,"first_page":2992,"last_page":2993,"pdf_url":2994,"poster_url":135,"slide_url":135,"video_url":135,"supplementary_url":135,"bibkey":2995,"paper_type":2658,"authors":2996,"abstract":3023},"lrec2026-ws-clinicalnlp-16","BDI at MEDIQA-EVAL 2026: A ReAct-Style Multimodal Agent for Fine-Grained Medical Response Assessment ","10.63317\u002F3xgwaboh563b","https:\u002F\u002Flrec.elra.info\u002Flrec2026-ws-clinicalnlp-16","141","152","http:\u002F\u002Fwww.lrec-conf.org\u002Fproceedings\u002Flrec2026\u002Fworkshops\u002Fclinicalnlp\u002Fpdf\u002F2026.clinicalnlp-1.16.pdf","xu-etal-2026-bdi",[2997,3000,3003,3006,3009,3012,3014,3017,3020],{"paper_id":2988,"author_seq":459,"given_name":2998,"surname":2999,"affiliation":135,"orcid":135},"Justin","Xu",{"paper_id":2988,"author_seq":434,"given_name":3001,"surname":3002,"affiliation":135,"orcid":135},"Zizheng","Zhang",{"paper_id":2988,"author_seq":408,"given_name":3004,"surname":3005,"affiliation":135,"orcid":135},"Augustine","Luk",{"paper_id":2988,"author_seq":387,"given_name":3007,"surname":3008,"affiliation":135,"orcid":135},"Benjamin","Khong",{"paper_id":2988,"author_seq":358,"given_name":3010,"surname":3011,"affiliation":135,"orcid":135},"Haochen","Cui",{"paper_id":2988,"author_seq":333,"given_name":3013,"surname":2725,"affiliation":135,"orcid":135},"Samuel",{"paper_id":2988,"author_seq":309,"given_name":3015,"surname":3016,"affiliation":135,"orcid":135},"Alyssa","Pradhan",{"paper_id":2988,"author_seq":280,"given_name":3018,"surname":3019,"affiliation":135,"orcid":135},"Kevin","Yuan",{"paper_id":2988,"author_seq":252,"given_name":3021,"surname":3022,"affiliation":135,"orcid":135},"David W.","Eyre","Free-text evaluation of multimodal clinical question answering (QA) systems remains a central challenge in medical NLP due to the complexity of medical knowledge, the necessity of integrating visual and textual information, and the limitations of existing automatic evaluation metrics for open-ended outputs. In this work, we present a training-free, agentic evaluation framework that formulates response scoring as evidence-guided orchestration of components rather than a task requiring conventional end-to-end fine-tuning of underlying LLMs\u002FVLMs. Our ReAct-style evaluator combines (i) structured reasoning, (ii) multimodal retrieval of similar encounters, (iii) auxiliary explainable feature-based regression models that provide numeric priors and human-interpretable signals, (iv) VLM-generated visual QA references for comparison, and (v) optional image augmentation tools. Unlike standard LLM-as-a-judge approaches that rely on direct generative scoring, our agent decomposes evaluation into modular stages of evidence acquisition, structured feature modeling, and integrative reasoning. We apply this architecture to the MEDIQA-EVAL shared task - a multimodal, multilingual clinical evaluation challenge that assesses system-generated answers for patient queries paired with images along multiple clinical quality dimensions. We report results across both English and Chinese tracks, comparing against baseline prompting methods, and discuss the feasibility and limitations of lightweight agentic systems for clinical QA evaluation.",{"paper_id":3025,"title":3026,"year":7,"month":358,"day":135,"doi":3027,"resource_url":3028,"first_page":3029,"last_page":3030,"pdf_url":3031,"poster_url":135,"slide_url":135,"video_url":135,"supplementary_url":135,"bibkey":3032,"paper_type":2658,"authors":3033,"abstract":3040},"lrec2026-ws-clinicalnlp-17","MasonNLP at MEDIQA-SYNUR 2026: Retrieval-Augmented Large Language Models for Schema-Constrained Clinical Information Extraction ","10.63317\u002F2xwjn5f2urna","https:\u002F\u002Flrec.elra.info\u002Flrec2026-ws-clinicalnlp-17","153","162","http:\u002F\u002Fwww.lrec-conf.org\u002Fproceedings\u002Flrec2026\u002Fworkshops\u002Fclinicalnlp\u002Fpdf\u002F2026.clinicalnlp-1.17.pdf","karim-etal-2026-masonnlp",[3034,3037],{"paper_id":3025,"author_seq":459,"given_name":3035,"surname":3036,"affiliation":135,"orcid":135},"A H M Rezaul","Karim",{"paper_id":3025,"author_seq":434,"given_name":3038,"surname":3039,"affiliation":135,"orcid":135},"Özlem","Uzuner","Conversational nurse-patient transcripts contain actionable observations, but converting these transcripts into structured representations at scale remains challenging. Documentation burden is substantial, with prior studies showing clinicians spend large portions of their workday on documentation and related desk work rather than direct patient care. MEDIQA-SYNUR focuses on observation extraction from conversational nurse-patient transcripts, requiring systems to normalize these narratives into a predefined schema with value-type constraints. We propose a modular retrieval-augmented generation (RAG) pipeline that uses the training set as an exemplar corpus, combines schema-constrained prompting (full schema vs. pruned candidate schema), deterministic schema-based postprocessing, and a second-pass audit, with two LLM backbones: Llama-4-Scout-17B-16E-Instruct and GPT-5.2 with corresponding embedding models for RAG. Our best configuration uses GPT-5.2 with full schema, RAG, and a second-pass auditing, achieving 80.36% F1 score. Overall, our results show that RAG consistently improves performance, while the optimal degree of schema constraint depends on the model, and second-pass auditing yields modest additional gains by correcting residual schema-adherence errors.",{"paper_id":3042,"title":3043,"year":7,"month":358,"day":135,"doi":3044,"resource_url":3045,"first_page":3046,"last_page":3047,"pdf_url":3048,"poster_url":135,"slide_url":135,"video_url":135,"supplementary_url":135,"bibkey":3049,"paper_type":2658,"authors":3050,"abstract":3062},"lrec2026-ws-clinicalnlp-18","Night Shift Nerds at MEDIQA-SYNUR 2026: Pushing Small Large Language Model Capability for Clinical Observation Extraction and Normalization from Nurse Dictation using RLVR ","10.63317\u002F328obtnwpmsw","https:\u002F\u002Flrec.elra.info\u002Flrec2026-ws-clinicalnlp-18","163","173","http:\u002F\u002Fwww.lrec-conf.org\u002Fproceedings\u002Flrec2026\u002Fworkshops\u002Fclinicalnlp\u002Fpdf\u002F2026.clinicalnlp-1.18.pdf","aryoyudanta-etal-2026-night",[3051,3054,3056,3059],{"paper_id":3042,"author_seq":459,"given_name":3052,"surname":3053,"affiliation":135,"orcid":135},"Bayu","Aryoyudanta",{"paper_id":3042,"author_seq":434,"given_name":373,"surname":3055,"affiliation":135,"orcid":135},"Yuliana",{"paper_id":3042,"author_seq":408,"given_name":3057,"surname":3058,"affiliation":135,"orcid":135},"Mikie","Rachman",{"paper_id":3042,"author_seq":387,"given_name":3060,"surname":3061,"affiliation":135,"orcid":135},"I Made Agus","Setiawan","We presented a small decoder-only language model for clinical observation extraction and normalization from nurse dictation developed using Reinforcement Learning with Verifiable Rewards (RLVR). We fine-tune Qwen3-1.7B model using a two-stage pipeline: (1) supervised fine-tuning (SFT) with an augmented chain-of-thought (CoT) dataset generated by a teacher model to mitigate RL cold-start, followed by (2) GRPO-based RLVR with multi-component reward functions that verify output format, concept presence, value type, and value correctness using the shared-task ontology (193 concepts) as a verifier. On the development set, SFT+GRPO substantially outperforms GRPO-only (F1 0.803 vs. 0.620). After the test holdout was released, our final system achieved 0.700 precision, 0.785 recall, and 0.740 F1. Error analysis shows remaining challenges in concept over-detection and missed concepts, as well as boundary errors in categorical and multi-select value type extraction. Our results demonstrates that small language models can enable accurate, cost-effective, and privacy preserving automated clinical documentation for nurse dictation, supporting scalable deployment in low-resource healthcare settings to reduce nurses’ documentation burden.",{"paper_id":3064,"title":3065,"year":7,"month":358,"day":135,"doi":3066,"resource_url":3067,"first_page":3068,"last_page":3069,"pdf_url":3070,"poster_url":135,"slide_url":135,"video_url":135,"supplementary_url":135,"bibkey":3071,"paper_type":2658,"authors":3072,"abstract":3082},"lrec2026-ws-clinicalnlp-19","Extracting Medication Instructions from Dutch General Practice Electronic Health Records with Local Natural Language Processing ","10.63317\u002F3stt4uepqdnq","https:\u002F\u002Flrec.elra.info\u002Flrec2026-ws-clinicalnlp-19","174","182","http:\u002F\u002Fwww.lrec-conf.org\u002Fproceedings\u002Flrec2026\u002Fworkshops\u002Fclinicalnlp\u002Fpdf\u002F2026.clinicalnlp-1.19.pdf","dukmak-etal-2026-extracting",[3073,3076,3079],{"paper_id":3064,"author_seq":459,"given_name":3074,"surname":3075,"affiliation":135,"orcid":135},"Marya","Dukmak",{"paper_id":3064,"author_seq":434,"given_name":3077,"surname":3078,"affiliation":135,"orcid":135},"Constanza L.","Andaur Navarro",{"paper_id":3064,"author_seq":408,"given_name":3080,"surname":3081,"affiliation":135,"orcid":135},"Artuur","Leeuwenberg","The extraction of structured medication prescription data from unstructured clinical text remains a critical challenge for clinical research and data standardization. This study investigates the application of Natural Language Processing (NLP) techniques to Dutch electronic health records (EHRs) from the Julius General Practitioners Network. The goal is to automatically extract key prescription attributes including dosage, duration, and medication unit and prepare them for integration into the ConcePTION Common Data Model, to support scalable pharmacoepidemiological research. We compare a lightweight rule-based system with transformer-based models (RobBERT and MedRoBERTa) under the technical constraints of a Trusted Research Environment, where external resources and cloud-based solutions are restricted. Using a dataset of 1,819 manually annotated records, the approaches are evaluated on predictive performance and computational costs. Results show that the rule-based system achieves strong accuracy and computational costs for structured patterns, while transformer-based models demonstrate greater robustness to linguistic variability. However, both approaches encounter difficulties with ambiguous dosage formats and long treatment durations. Our findings indicate that NLP methods can substantially improve the structuring of Dutch prescription data and support scalable pharmacoepidemiological research. Future work should focus on improving generalization and expanding annotated datasets to enhance model reliability.",{"paper_id":3084,"title":3085,"year":7,"month":358,"day":135,"doi":3086,"resource_url":3087,"first_page":3088,"last_page":3089,"pdf_url":3090,"poster_url":135,"slide_url":135,"video_url":135,"supplementary_url":135,"bibkey":3091,"paper_type":2658,"authors":3092,"abstract":3098},"lrec2026-ws-clinicalnlp-20","Gladiator at MEDIQA-SYNUR 2026: Contextual Clinical Extraction: Integrating Foundation Models with Domain-Specific Validation Rules ","10.63317\u002F2tkvpwgzzcn2","https:\u002F\u002Flrec.elra.info\u002Flrec2026-ws-clinicalnlp-20","183","191","http:\u002F\u002Fwww.lrec-conf.org\u002Fproceedings\u002Flrec2026\u002Fworkshops\u002Fclinicalnlp\u002Fpdf\u002F2026.clinicalnlp-1.20.pdf","pusapati-etal-2026-gladiator",[3093,3096],{"paper_id":3084,"author_seq":459,"given_name":3094,"surname":3095,"affiliation":135,"orcid":135},"Siva Satyanarayana Raju","Pusapati",{"paper_id":3084,"author_seq":434,"given_name":3097,"surname":1794,"affiliation":135,"orcid":135},"Ankit","We present a hybrid extraction system that combines large language model capabilities with rule-based precision for extracting structured clinical observations from nursing dictation transcripts. Our approach leverages Claude Opus 4.5 as the primary extractor, enhanced with comprehensive prompt engineering that includes the complete 193-concept schema, few-shot examples, and detailed validation rules covering respiratory, cardiac, diagnosis, and mental status fields. The LLM output undergoes extensive post-processing with six specialized filters that remove speculative diagnoses, validate physiological ranges, ensure unit-field dependencies, and verify contextual appropriateness. Five correction mechanisms normalize breathing patterns, map dyspnea severity, standardize assistance levels, clean STRING fields, and handle multi-select conjunctions. A supplementary rule-based component employs 400+ regex patterns with contextual validation to capture high-confidence observations, particularly for vital signs and categorical fields. The system requires cardiac keywords for heart rate extraction and respiratory context for respiration rates, preventing false positives from unrelated numeric values. Results are merged through an intelligent strategy that prioritizes LLM comprehensiveness while supplementing with rule-based findings. A strict schema validation layer ensures all four value types (NUMERIC, STRING, SINGLE_SELECT, MULTI_SELECT) conform to enumerated options and physiological ranges. This multi-layered approach balances recall through LLM reasoning with precision through rule-based validation, effectively structuring natural nursing narratives into standardized EHR-ready observations.",{"paper_id":3100,"title":3101,"year":7,"month":358,"day":135,"doi":3102,"resource_url":3103,"first_page":3104,"last_page":3105,"pdf_url":3106,"poster_url":135,"slide_url":135,"video_url":135,"supplementary_url":135,"bibkey":3107,"paper_type":2658,"authors":3108,"abstract":3114},"lrec2026-ws-clinicalnlp-21","MedAware at MEDIQA-EVAL 2026: Vision-Language Model Fine-Tuning with Logprob-Based Score Calibration for Medical Response Evaluation ","10.63317\u002F3pcaf428rnrm","https:\u002F\u002Flrec.elra.info\u002Flrec2026-ws-clinicalnlp-21","192","199","http:\u002F\u002Fwww.lrec-conf.org\u002Fproceedings\u002Flrec2026\u002Fworkshops\u002Fclinicalnlp\u002Fpdf\u002F2026.clinicalnlp-1.21.pdf","hao-etal-2026-medaware",[3109,3112],{"paper_id":3100,"author_seq":459,"given_name":3110,"surname":3111,"affiliation":135,"orcid":135},"Ziqi","Hao",{"paper_id":3100,"author_seq":434,"given_name":3113,"surname":1257,"affiliation":135,"orcid":135},"Pengbo","We present MedAware, our MEDIQA-EVAL 2026 system for predicting human ratings of medical QA responses from text and images. We fine-tune Qwen3-VL models (4B\u002F8B\u002F32B) with supervised fine-tuning (SFT), and study GRPO as an optional second stage under both LoRA and full-parameter settings. To handle severe label skew and unstable correlation metrics, we use logprob-based continuous scoring with quantile calibration, converting token probabilities into calibrated metric scores without retraining. This reduces prediction collapse on skewed dimensions and improves metric stability in both English and Chinese. The approach follows the official reference-based shared-task setup and is designed to produce meaningful metric estimates even under extreme class imbalance. In the official shared-task submission setting (8B-LoRA SFT with discrete scoring), our system ranked 3rd on English and 1st among participants on Chinese. Separately, in post-competition offline re-evaluations with logprob scoring, the best tested configuration reaches 0.449 EN-ALL and 0.308 ZH-ALL, while SFT initialization remains critical for effective GRPO.",{"paper_id":3116,"title":3117,"year":7,"month":358,"day":135,"doi":3118,"resource_url":3119,"first_page":3120,"last_page":3121,"pdf_url":3122,"poster_url":135,"slide_url":135,"video_url":135,"supplementary_url":135,"bibkey":3123,"paper_type":2658,"authors":3124,"abstract":3128},"lrec2026-ws-clinicalnlp-22","HSE NLP TEAM at MEDIQA-SYNUR 2026: Consensus Adjudication Ensemble (ACE): Balancing Precision and Recall for Schema-Bystander Clinical Extraction ","10.63317\u002F4vsjfsrfx246","https:\u002F\u002Flrec.elra.info\u002Flrec2026-ws-clinicalnlp-22","200","211","http:\u002F\u002Fwww.lrec-conf.org\u002Fproceedings\u002Flrec2026\u002Fworkshops\u002Fclinicalnlp\u002Fpdf\u002F2026.clinicalnlp-1.22.pdf","valiev-2026-hse",[3125],{"paper_id":3116,"author_seq":459,"given_name":3126,"surname":3127,"affiliation":135,"orcid":135},"Airat A.","Valiev","Clinical documentation from nurse dictations is labor-intensive and error-prone, yet it contains high-value observations that must be transferred into structured flowsheets. The MEDIQA-SYNUR 2026 shared task evaluates systems that extract and ontology-align 193 clinical concepts (with heterogeneous value types) from synthetic speech transcripts derived from intensive care notes. We describe the Consensus Adjudication Ensemble (ACE), a three-stage pipeline that (i) maximizes candidate coverage via complementary generators, (ii) enforces high precision through a dedicated adjudicator that operates as a verifier rather than a generator, and (iii) restores strict schema compliance using a targeted, token-efficient repair step. On the official test set we achieve an exact-match micro-F1 of 0.7996 (P=0.7812, R=0.8188), ranking 4th on the leaderboard. Beyond the competitive result, we analyze clinically relevant failure modes - hallucinated interventions, over-confident categorical labels, and unit\u002Fnormalization errors - and quantify adjudication trade-offs: 2,219 candidates removed, 91.3% of which are true false positives, at the cost of 8.7% mistakenly removed true positives. Finally, targeted schema repair reduces validation context from approx. 230k tokens to \u003C2k per document while preserving most extraction gains. Keywords: clinical information extraction, nurse dictations, ontology alignment, ensemble methods, adjudication, error analysis",{"paper_id":3130,"title":3131,"year":7,"month":358,"day":135,"doi":3132,"resource_url":3133,"first_page":3134,"last_page":3135,"pdf_url":3136,"poster_url":135,"slide_url":135,"video_url":135,"supplementary_url":135,"bibkey":3137,"paper_type":2658,"authors":3138,"abstract":3154},"lrec2026-ws-clinicalnlp-23","Lakefront AI Ramblers at MEDIQA-SYNUR 2026: Hybrid Retrieval and LLM Verification for Open-Source Schema-Guided Clinical Information Extraction ","10.63317\u002F2jfca3zi8p4o","https:\u002F\u002Flrec.elra.info\u002Flrec2026-ws-clinicalnlp-23","212","221","http:\u002F\u002Fwww.lrec-conf.org\u002Fproceedings\u002Flrec2026\u002Fworkshops\u002Fclinicalnlp\u002Fpdf\u002F2026.clinicalnlp-1.23.pdf","saban-etal-2026-lakefront",[3139,3142,3145,3148,3151],{"paper_id":3130,"author_seq":459,"given_name":3140,"surname":3141,"affiliation":135,"orcid":135},"Michael T.","Saban",{"paper_id":3130,"author_seq":434,"given_name":3143,"surname":3144,"affiliation":135,"orcid":135},"Arsalan","Yaghoubi",{"paper_id":3130,"author_seq":408,"given_name":3146,"surname":3147,"affiliation":135,"orcid":135},"Behnaz","Eslami",{"paper_id":3130,"author_seq":387,"given_name":3149,"surname":3150,"affiliation":135,"orcid":135},"Samie","Tootooni",{"paper_id":3130,"author_seq":358,"given_name":3152,"surname":3153,"affiliation":135,"orcid":135},"Dmitriy","Dligach","Schema-constrained clinical information extraction requires identifying text-supported observations and outputting exact schema identifiers and values. In the MEDIQA-SYNUR 2026 shared task, synthetic nursing dictations were mapped to structured JSON outputs aligned with a 193-concept clinical schema under strict exact-match evaluation. We extended the baseline pipeline, which consists of transcript segmentation, schema retrieval, and LLM-based extraction, with hybrid schema retrieval, supervised fine-tuning (SFT) of open-source LLMs, and LLM-based verification. Our hybrid retrieval approach combined dense embeddings with sparse BM25 representations using a convex combination strategy, improving schema coverage to 0.994 recall@60 on the development set. We evaluated GPT-4o, GPT-4o-mini, Llama-3-8B-Instruct, and Llama-3.3-70B-Instruct, applying LoRA-based SFT to open-source models. On the official test set, our best submitted configuration (Llama-3.3-70B-Instruct-SFT with union voting and GPT-4o-mini verification) achieved 0.711 F1. Post-competition experiments showed that Llama-3-8B-Instruct-SFT (train + dev) reached 0.723 F1 under the same post-processing pipeline. For reference, GPT-4o achieved 0.791 F1 and did not benefit from post-processing. Performance differences across development and test splits further highlight the sensitivity of post-processing strategies to variation across split distribution. Overall, integrating high-recall retrieval, SFT, and LLM verification substantially narrows the performance gap between open- and closed-source models for schema guided clinical extraction.",{"paper_id":3156,"title":3157,"year":7,"month":358,"day":135,"doi":3158,"resource_url":3159,"first_page":3160,"last_page":3161,"pdf_url":3162,"poster_url":135,"slide_url":135,"video_url":135,"supplementary_url":135,"bibkey":3163,"paper_type":2658,"authors":3164,"abstract":3180},"lrec2026-ws-clinicalnlp-24","JMedWiC: A Japanese Word-in-Context Dataset in the Medical Domain ","10.63317\u002F2wqoixeze6fo","https:\u002F\u002Flrec.elra.info\u002Flrec2026-ws-clinicalnlp-24","222","227","http:\u002F\u002Fwww.lrec-conf.org\u002Fproceedings\u002Flrec2026\u002Fworkshops\u002Fclinicalnlp\u002Fpdf\u002F2026.clinicalnlp-1.24.pdf","horiguchi-etal-2026-jmedwic",[3165,3168,3171,3174,3177],{"paper_id":3156,"author_seq":459,"given_name":3166,"surname":3167,"affiliation":135,"orcid":135},"Koki","Horiguchi",{"paper_id":3156,"author_seq":434,"given_name":3169,"surname":3170,"affiliation":135,"orcid":135},"Seiji","Sugiyama",{"paper_id":3156,"author_seq":408,"given_name":3172,"surname":3173,"affiliation":135,"orcid":135},"Tomoyuki","Kajiwara",{"paper_id":3156,"author_seq":387,"given_name":3175,"surname":3176,"affiliation":135,"orcid":135},"Shoko","Wakamiya",{"paper_id":3156,"author_seq":358,"given_name":3178,"surname":3179,"affiliation":135,"orcid":135},"Eiji","Aramaki","We release JMedWiC, a Japanese dataset for Word-in-Context (WiC) tasks specifically tailored to the medical domain. To address the challenge of word sense disambiguation, where the meaning of a word varies depending on its context, previous research has developed WiC datasets to evaluate word sense identity by determining whether a target word shares the same sense across two given contexts. In the medical domain, the misinterpretation of word senses can hinder the accurate comprehension of medical information; however, there is currently no Japanese WiC dataset specialized for this domain. Moreover, existing WiC datasets have been constructed using lexical resources with sense inventories, such as WordNet and UMLS, but such resources are not sufficiently developed for Japanese. Therefore, we construct a Japanese WiC dataset in the medical domain by manually annotating sense-identity labels for target words in context pairs automatically extracted from a large-scale corpus, without relying on lexical resources.",{"paper_id":3182,"title":3183,"year":7,"month":358,"day":135,"doi":3184,"resource_url":3185,"first_page":3186,"last_page":3187,"pdf_url":3188,"poster_url":135,"slide_url":135,"video_url":135,"supplementary_url":135,"bibkey":3189,"paper_type":2658,"authors":3190,"abstract":3199},"lrec2026-ws-clinicalnlp-25","LTRC-Medicom at MEDIQA-SYNUR 2026: Schema-Guided Clinical Information Extraction with Hybrid Clustering-SFT-Verification ","10.63317\u002F4oou5ss2efr6","https:\u002F\u002Flrec.elra.info\u002Flrec2026-ws-clinicalnlp-25","228","234","http:\u002F\u002Fwww.lrec-conf.org\u002Fproceedings\u002Flrec2026\u002Fworkshops\u002Fclinicalnlp\u002Fpdf\u002F2026.clinicalnlp-1.25.pdf","deepak-etal-2026-ltrc",[3191,3193,3196],{"paper_id":3182,"author_seq":459,"given_name":3192,"surname":1895,"affiliation":135,"orcid":135},"Pasumarthy",{"paper_id":3182,"author_seq":434,"given_name":3194,"surname":3195,"affiliation":135,"orcid":135},"Sushvin","Marimuthu",{"paper_id":3182,"author_seq":408,"given_name":3197,"surname":3198,"affiliation":135,"orcid":135},"Parameswari","Krishnamurthy","Extracting structured clinical data from unstructured patient transcripts is challenging due to large target schemas and inherent linguistic ambiguity. We address the extraction of 193 heterogeneous clinical attributes from nursing notes and clinician–patient dialogues, and demonstrate that zero-shot large language models (LLMs) are ineffective in this setting, achieving an F1 score below 0.15 due to context window saturation and hallucination. We propose a four-stage framework that combines semantic schema clustering, role-based chain-of-thought prompting, supervised fine-tuning of Llama-3.1-8B, and transcript-verified post-processing. Our approach achieves an F1 score of 0.66, representing a 4.4x improvement over the baseline, by balancing high recall from generative models with high precision from verification. These results highlight the effectiveness of hybrid pipelines for high-stakes clinical information extraction.",{"paper_id":3201,"title":3202,"year":7,"month":358,"day":135,"doi":3203,"resource_url":3204,"first_page":3205,"last_page":3206,"pdf_url":3207,"poster_url":135,"slide_url":135,"video_url":135,"supplementary_url":135,"bibkey":3208,"paper_type":2658,"authors":3209,"abstract":3225},"lrec2026-ws-clinicalnlp-26","SloCal-Net at MEDIQA-Eval 2026: Investigating the Impact of Reasoning and External Context on Medical Answer Grading ","10.63317\u002F57g3cfef3xcd","https:\u002F\u002Flrec.elra.info\u002Flrec2026-ws-clinicalnlp-26","235","243","http:\u002F\u002Fwww.lrec-conf.org\u002Fproceedings\u002Flrec2026\u002Fworkshops\u002Fclinicalnlp\u002Fpdf\u002F2026.clinicalnlp-1.26.pdf","kocbek-etal-2026-slocal",[3210,3213,3216,3219,3222],{"paper_id":3201,"author_seq":459,"given_name":3211,"surname":3212,"affiliation":135,"orcid":135},"Primoz","Kocbek",{"paper_id":3201,"author_seq":434,"given_name":3214,"surname":3215,"affiliation":135,"orcid":135},"Valentina","Carbonari",{"paper_id":3201,"author_seq":408,"given_name":3217,"surname":3218,"affiliation":135,"orcid":135},"Pierangelo","Veltri",{"paper_id":3201,"author_seq":387,"given_name":3220,"surname":3221,"affiliation":135,"orcid":135},"Pietro Hiram","Guzzi",{"paper_id":3201,"author_seq":358,"given_name":3223,"surname":3224,"affiliation":135,"orcid":135},"Gregor","Stiglic","Automated evaluation of multimodal medical answers is essential for scalable safety assessment, yet it remains difficult to align automatic scores with expert judgment across languages and image modalities. We describe SloCal-Net’s systems for the MEDIQA-EVAL 2026 shared task, framing evaluation as rubric-conditioned multimodal judging: the judge receives the question, image(s), candidate answer, and task-specific criteria, and outputs criterion-level scores and an overall rating. Evidence retrieval was initialized using ChatGPT Deep Research, producing a 25-document clinical corpus used for lightweight retrieval-augmented grounding. On the official leaderboard, our best submission (GPT-5-mini with web search and RAG) achieved Pearson correlations of 0.466 on English and 0.260 on Chinese expert ratings. In post-competition experiments with open-source judges, the best English Pearson reached 0.272 with GLM-4.6V and 0.212 with Qwen3-VL-30B-Thinking, while Chinese correlations were lower, highlighting remaining gaps in multilingual calibration and image–text grounding.",{"paper_id":3227,"title":3228,"year":7,"month":358,"day":135,"doi":3229,"resource_url":3230,"first_page":3231,"last_page":3232,"pdf_url":3233,"poster_url":135,"slide_url":135,"video_url":135,"supplementary_url":135,"bibkey":3234,"paper_type":2658,"authors":3235,"abstract":3242},"lrec2026-ws-clinicalnlp-27","MIDAS_SYNUR at MEDIQA-SYNUR 2026: A Prompting Study for Clinical Observation Extraction from Nurse Dictation Transcriptions ","10.63317\u002F4h7qk3596z83","https:\u002F\u002Flrec.elra.info\u002Flrec2026-ws-clinicalnlp-27","244","250","http:\u002F\u002Fwww.lrec-conf.org\u002Fproceedings\u002Flrec2026\u002Fworkshops\u002Fclinicalnlp\u002Fpdf\u002F2026.clinicalnlp-1.27.pdf","sriram-etal-2026-midas_synur",[3236,3239],{"paper_id":3227,"author_seq":459,"given_name":3237,"surname":3238,"affiliation":135,"orcid":135},"Swetha Krishna","Sriram",{"paper_id":3227,"author_seq":434,"given_name":3240,"surname":3241,"affiliation":135,"orcid":135},"Akshitaa","Sahoo","This paper describes MIDAS_SYNUR, a system developed for the MEDIQA-SYNUR task at ClinicalNLP 2026 on observation extraction from nurse dictations. The primary system adopts a single-prompt, field-rich few-shot strategy using GPT-5.2, jointly generating all schema fields in one structured output. Few-shot demonstrations are curated and grouped by value type, with five examples per type, promoting consistency across heterogeneous value distributions while leveraging global context to resolve cross-field dependencies. To analyze design trade-offs, this holistic strategy is compared against a field-wise decomposed prompting baseline, where each schema field is extracted independently using explicit positive and NULL demonstrations to improve absence detection and reduce cross-field interference. Zero-shot variants of both approaches are also evaluated to isolate the contribution of in-context examples. The results highlight inference-time prompting as a simple, reproducible, and competitive baseline for large-scale clinical observation extraction from conversational nurse dictations. Keywords: Prompt Engineering, Few-Shot Prompting, In-Context Learning, Structured Output",{"paper_id":3244,"title":3245,"year":7,"month":358,"day":135,"doi":3246,"resource_url":3247,"first_page":3248,"last_page":3249,"pdf_url":3250,"poster_url":135,"slide_url":135,"video_url":135,"supplementary_url":135,"bibkey":3251,"paper_type":2658,"authors":3252,"abstract":3256},"lrec2026-ws-clinicalnlp-28","AnotherOne at MEDIQA-SYNUR 2026: Detect, Extract, Normalize - Knowledge-Grounded LLM Pipeline for Clinical Observation Extraction ","10.63317\u002F5hdskbdu4usi","https:\u002F\u002Flrec.elra.info\u002Flrec2026-ws-clinicalnlp-28","251","256","http:\u002F\u002Fwww.lrec-conf.org\u002Fproceedings\u002Flrec2026\u002Fworkshops\u002Fclinicalnlp\u002Fpdf\u002F2026.clinicalnlp-1.28.pdf","thomas-etal-2026-anotherone",[3253,3255],{"paper_id":3244,"author_seq":459,"given_name":3254,"surname":1723,"affiliation":135,"orcid":135},"Jerrin John",{"paper_id":3244,"author_seq":434,"given_name":3197,"surname":3198,"affiliation":135,"orcid":135},"We present a system for the MEDIQA-SYNUR 2026 shared task on extracting structured clinical observations from nurse dictation transcripts. The transcripts contain spoken-style clinical language with disfluencies, filler words, and hesitations. Our approach is a four-stage LLM inference pipeline preceded by an offline knowledge enhancement step: (1) knowledge-enhanced concept detection using medical domain clustering, (2) evidence-grounded value extraction, (3) schema-constrained value normalization, and (4) deterministic post-processing with fuzzy matching and unit pairing. In the offline step, we use the task ontology and training examples to generate per-concept clinical definitions and extraction rules, and group the 193 concepts into 19 non-exclusive medical domain clusters. These are injected into all downstream prompts as domain priors. All LLM stages use gpt-oss-120b with structured JSON output and chain-of-thought reasoning. The task requires exact matching on concept ID and value pairs across a 193-concept ontology, making precision particularly challenging. We iteratively refine concept definitions and prompt guidelines based on error analysis of the training data. Our system achieves an F1 score of 0.806 on the test set.",{"paper_id":3258,"title":3259,"year":7,"month":358,"day":135,"doi":3260,"resource_url":3261,"first_page":3262,"last_page":3263,"pdf_url":3264,"poster_url":135,"slide_url":135,"video_url":135,"supplementary_url":135,"bibkey":3265,"paper_type":2658,"authors":3266,"abstract":3270},"lrec2026-ws-clinicalnlp-29","hgkai26 at MEDIQA-EVAL 2026: Automated Evaluation of Visual Medical Question Answering Using LLM-as-a-Judge ","10.63317\u002F4n9skmf9rive","https:\u002F\u002Flrec.elra.info\u002Flrec2026-ws-clinicalnlp-29","257","261","http:\u002F\u002Fwww.lrec-conf.org\u002Fproceedings\u002Flrec2026\u002Fworkshops\u002Fclinicalnlp\u002Fpdf\u002F2026.clinicalnlp-1.29.pdf","gangavarapu-2026-hgkai26",[3267],{"paper_id":3258,"author_seq":459,"given_name":3268,"surname":3269,"affiliation":135,"orcid":135},"Haritha","Gangavarapu","As there is a rise in the use of multimodal large language models (LLMs) for medical response generation, it is necessary to have reliable automated evaluation mechanisms that can assess the quality of model-generated outputs. The MediQA-Eval 2026 shared task focuses on grading AI-generated dermatology and wound care responses using structured human-aligned rubrics. In this work, we explore a zero-shot multimodal LLM-as-a-Judge framework to assess candidate responses across multiple quality dimensions. System performance is evaluated using the official task metrics designed to reflect alignment with human judgments. Our findings provide preliminary insights into the feasibility and limitations of LLM-based evaluators for rubric-guided medical response assessment.",{"paper_id":3272,"title":3273,"year":7,"month":358,"day":135,"doi":3274,"resource_url":3275,"first_page":3276,"last_page":3277,"pdf_url":3278,"poster_url":135,"slide_url":135,"video_url":135,"supplementary_url":135,"bibkey":3279,"paper_type":2658,"authors":3280,"abstract":3290},"lrec2026-ws-clinicalnlp-30","Role-Adapted Clinical Report Generation for Ultrasound Measurements in Low-Resource Settings ","10.63317\u002F28pa52zsmb39","https:\u002F\u002Flrec.elra.info\u002Flrec2026-ws-clinicalnlp-30","262","269","http:\u002F\u002Fwww.lrec-conf.org\u002Fproceedings\u002Flrec2026\u002Fworkshops\u002Fclinicalnlp\u002Fpdf\u002F2026.clinicalnlp-1.30.pdf","nainia-etal-2026-role",[3281,3282,3285,3288],{"paper_id":3272,"author_seq":459,"given_name":2783,"surname":2784,"affiliation":135,"orcid":135},{"paper_id":3272,"author_seq":434,"given_name":3283,"surname":3284,"affiliation":135,"orcid":135},"Tanya","Akumu",{"paper_id":3272,"author_seq":408,"given_name":3286,"surname":3287,"affiliation":135,"orcid":135},"Noussair","Lazrak",{"paper_id":3272,"author_seq":387,"given_name":3036,"surname":3289,"affiliation":135,"orcid":135},"Lekadir","Obstetric ultrasound is critical for monitoring fetal growth, yet in many low-resource settings, healthcare workers who perform or receive ultrasound measurements lack the training to interpret them clinically. We present a system that automatically generates role-adapted clinical reports from fetal biometry measurements, targeting six healthcare worker roles across three expertise levels. The system combines Retrieval-Augmented Generation (RAG) from a knowledge base extracted from the World Health Organization (WHO) Manual of Diagnostic Ultrasound with deterministic fetal growth percentile computation based on INTERGROWTH-21st international standards. The knowledge base is designed for multilingual extensibility: since the source material is from an official WHO document, entries can be translated into any target language by domain experts or machine translation services. A key design principle is that clinical decision support (red, yellow, and green alerts) is derived deterministically from percentile thresholds, not from the language model, ensuring safety regardless of LLM output quality. Evaluation demonstrates sub-millimeter accuracy in percentile computation, 100% correctness in decision support classification, measurable readability differentiation across roles (Flesch-Kincaid grade 8.8 for community health workers vs. 11-13 for clinical roles), and 98% factual consistency across 42 generated reports spanning seven clinical scenarios. The system is designed for local deployment without internet connectivity.",{"paper_id":3292,"title":3293,"year":7,"month":358,"day":135,"doi":3294,"resource_url":3295,"first_page":3296,"last_page":3297,"pdf_url":3298,"poster_url":135,"slide_url":135,"video_url":135,"supplementary_url":135,"bibkey":3299,"paper_type":2658,"authors":3300,"abstract":3313},"lrec2026-ws-clinicalnlp-31","A Comparative Study of Approaches to Anonymization of Clinical Free Text in Spanish ","10.63317\u002F26nxpzmf4966","https:\u002F\u002Flrec.elra.info\u002Flrec2026-ws-clinicalnlp-31","270","280","http:\u002F\u002Fwww.lrec-conf.org\u002Fproceedings\u002Flrec2026\u002Fworkshops\u002Fclinicalnlp\u002Fpdf\u002F2026.clinicalnlp-1.31.pdf","brunello-etal-2026-comparative",[3301,3304,3307,3310],{"paper_id":3292,"author_seq":459,"given_name":3302,"surname":3303,"affiliation":135,"orcid":135},"Florencia Luciana","Brunello",{"paper_id":3292,"author_seq":434,"given_name":3305,"surname":3306,"affiliation":135,"orcid":135},"Laura","Alonso Alemany",{"paper_id":3292,"author_seq":408,"given_name":3308,"surname":3309,"affiliation":135,"orcid":135},"Serena","Villata",{"paper_id":3292,"author_seq":387,"given_name":3311,"surname":3312,"affiliation":135,"orcid":135},"Milagro","Teruel","The anonymization of clinical free-text records is a prerequisite for enabling the secondary use of healthcare data while preserving patient privacy. This challenge is particularly acute for Spanish clinical text, where annotated resources are scarce and practitioners lack clear empirical guidance on which technological approaches are more adequate to their particular restrictions and capabilities. In this work, we present a controlled comparative study of representative anonymization paradigms for Spanish clinical narratives, including a baseline rule-based approach, a general-purpose large language model under prompt-based inference, an off-the-shelf industrial NLP toolkit (spaCy) and comparable neural sequence labeling architectures. To ensure a fair and contamination-aware evaluation, particularly given the opacity of pretrained model training data, we introduce a synthetic clinical dataset. Recurrent neural network architectures, particularly the off-the-shelf spaCy toolkit, consistently achieve the best balance between effectiveness, computational efficiency, and deployment feasibility. We further observe that training task-specific embeddings end-to-end yields stronger generalization than incorporating pretrained representations. Although limited to Spanish and to representative instances of each paradigm, the study identifies stable performance tendencies across datasets. These results provide actionable guidance for institutions seeking to implement anonymization pipelines. This work contributes reproducible evaluation procedures and empirical evidence for privacy-preserving clinical NLP in Spanish.",{"paper_id":3315,"title":3316,"year":7,"month":358,"day":135,"doi":3317,"resource_url":3318,"first_page":3319,"last_page":3320,"pdf_url":3321,"poster_url":135,"slide_url":135,"video_url":135,"supplementary_url":135,"bibkey":3322,"paper_type":2658,"authors":3323,"abstract":3335},"lrec2026-ws-clinicalnlp-32","Disagreement-Driven Joint Refinement of Retrieval and Decision Rules for Imbalanced Counseling Risk Classification ","10.63317\u002F2f8zttankw7p","https:\u002F\u002Flrec.elra.info\u002Flrec2026-ws-clinicalnlp-32","281","289","http:\u002F\u002Fwww.lrec-conf.org\u002Fproceedings\u002Flrec2026\u002Fworkshops\u002Fclinicalnlp\u002Fpdf\u002F2026.clinicalnlp-1.32.pdf","shao-etal-2026-disagreement",[3324,3327,3329,3332],{"paper_id":3315,"author_seq":459,"given_name":3325,"surname":3326,"affiliation":135,"orcid":135},"Zhihao","Shao",{"paper_id":3315,"author_seq":434,"given_name":1950,"surname":3328,"affiliation":135,"orcid":135},"Sekizaki",{"paper_id":3315,"author_seq":408,"given_name":3330,"surname":3331,"affiliation":135,"orcid":135},"Shengzhou","Yi",{"paper_id":3315,"author_seq":387,"given_name":3333,"surname":3334,"affiliation":135,"orcid":135},"Toshihiko","Yamasaki","With the rapid growth of online counseling services, timely and reliable risk classification of counseling records is essential for supporting early screening and prioritizing limited intervention resources. High-risk samples refer to high-acuity suicide risk and require expedited human review. However, this task is challenging due to severe class imbalance (93% low-risk and 7% high-risk samples) and complex decision boundaries. Large language models (LLMs) exhibit unstable predictions and systematic errors in such imbalanced clinical-text settings. To address this issue, we propose Disagreement-Driven Joint Refinement (DDJR), an iterative, parameter-free refinement framework. It uses prediction disagreement between two inference settings, zero-shot and retrieval-augmented in-context learning, as the primary signal for identifying high-value instances. These disagreement-identified instances are transformed into adaptive refinement signals and used to jointly update both the exemplar pool and an executable rule set, thereby sharpening decision boundaries and improving prediction stability. Experiments on 6,481 real-world counseling records demonstrate that the proposed DDJR outperforms existing methods, achieving an accuracy of 0.915 and a Matthews Correlation Coefficient (MCC) of 0.583. These results demonstrate that DDJR achieves more stable and reliable predictions for high-stakes counseling risk classification in real-world settings.",{"paper_id":3337,"title":3338,"year":7,"month":358,"day":135,"doi":3339,"resource_url":3340,"first_page":3341,"last_page":3342,"pdf_url":3343,"poster_url":135,"slide_url":135,"video_url":135,"supplementary_url":135,"bibkey":3344,"paper_type":2658,"authors":3345,"abstract":3366},"lrec2026-ws-clinicalnlp-33","Context-Aware SNOMED CT Entity Linking for Clinical Text ","10.63317\u002F5am2vrdigksi","https:\u002F\u002Flrec.elra.info\u002Flrec2026-ws-clinicalnlp-33","290","299","http:\u002F\u002Fwww.lrec-conf.org\u002Fproceedings\u002Flrec2026\u002Fworkshops\u002Fclinicalnlp\u002Fpdf\u002F2026.clinicalnlp-1.33.pdf","kadusabe-etal-2026-context",[3346,3349,3352,3355,3358,3360,3363],{"paper_id":3337,"author_seq":459,"given_name":3347,"surname":3348,"affiliation":135,"orcid":135},"Provia","Kadusabe",{"paper_id":3337,"author_seq":434,"given_name":3350,"surname":3351,"affiliation":135,"orcid":135},"Demian","Gholipour Ghalandari",{"paper_id":3337,"author_seq":408,"given_name":3353,"surname":3354,"affiliation":135,"orcid":135},"Lauren","Cassidy",{"paper_id":3337,"author_seq":387,"given_name":3356,"surname":3357,"affiliation":135,"orcid":135},"Jack","Boylan",{"paper_id":3337,"author_seq":358,"given_name":579,"surname":3359,"affiliation":135,"orcid":135},"Hokamp",{"paper_id":3337,"author_seq":333,"given_name":3361,"surname":3362,"affiliation":135,"orcid":135},"Abhishek","Kaushik",{"paper_id":3337,"author_seq":309,"given_name":3364,"surname":3365,"affiliation":135,"orcid":135},"Fiona","Lawless","Mapping free-text mentions in clinical notes to standardized terminologies such as SNOMED CT is essential for large-scale secondary use of electronic health records, but remains challenging due to linguistic variability, under-specified annotation guidelines, term ambiguity, and ontology scale. This work presents a two-stage entity linking pipeline that combines span detection with context-aware concept linking and evaluates it on the SNOMED CT Entity Linking Challenge dataset. Our work builds upon the SNOMED CT entity linking challenge , resulting in a fully open-source system. To our knowledge, this is the first end-to-end open-source system for this task. For span detection, we compare multiple neural architectures together with dictionary-based matching. For concept linking, we adopt a context-aware bi-encoder, and construct a multi-source knowledge base enriched with context derived from the SNOMED CT ontology. Finally, we implement an agentic re-ranker and test the effectiveness of LLM-backed re-ranking with access to annotation guidelines. In contrast to findings from the original shared task submissions, we show that context is important for optimal performance, and that agentic re-ranking with a state-of-the-art LLM only marginally improves overall performance, suggesting that the current benchmark may be approaching its practical ceiling. This work provides the first fully open-source, reproducible system for SNOMED CT entity linking, offering a foundation for future research and practical deployment.",{"paper_id":3368,"title":3369,"year":7,"month":358,"day":135,"doi":3370,"resource_url":3371,"first_page":3372,"last_page":3373,"pdf_url":3374,"poster_url":135,"slide_url":135,"video_url":135,"supplementary_url":135,"bibkey":3375,"paper_type":2658,"authors":3376,"abstract":3386},"lrec2026-ws-clinicalnlp-34","Temporal Structure in Clinical Narratives in Portuguese: Insights from Cross-Document Annotation ","10.63317\u002F49rdto6y6uro","https:\u002F\u002Flrec.elra.info\u002Flrec2026-ws-clinicalnlp-34","300","312","http:\u002F\u002Fwww.lrec-conf.org\u002Fproceedings\u002Flrec2026\u002Fworkshops\u002Fclinicalnlp\u002Fpdf\u002F2026.clinicalnlp-1.34.pdf","fernandes-etal-2026-temporal",[3377,3380,3383],{"paper_id":3368,"author_seq":459,"given_name":3378,"surname":3379,"affiliation":135,"orcid":135},"Ana Luisa","Fernandes",{"paper_id":3368,"author_seq":434,"given_name":3381,"surname":3382,"affiliation":135,"orcid":135},"Purificação","Silvano",{"paper_id":3368,"author_seq":408,"given_name":3384,"surname":3385,"affiliation":135,"orcid":135},"Luís Filipe","Cunha","Medical reports, by documenting disease progression and patient responses to treatment, form continuous narratives in which each new document adds a chapter to the patient’s clinical story. Constructing coherent patient timelines requires identifying temporal relations across multiple medical reports that compose a patient’s clinical journey. However, cross-document temporal annotation remains an underexplored area, largely due to the methodological and conceptual challenges it entails. This study addresses these challenges by investigating the identification and characterization of cross-document temporal relations in Portuguese medical records. For this purpose, cross-document annotation was performed on different types of reports (Group Consultation Reports, Discharge Reports, and General Reports) from patients diagnosed with Acute Myeloid Leukemia and followed at IPO-Porto, Portugal. Annotation was carried out using the Med2Story scheme, specifically designed to capture both temporal and medical information. Our results indicate that, although cross-document annotation of temporal information is more demanding in terms of both the annotation scheme and the annotation process, it enables the construction of coherent chronological representations of patients’ clinical journeys. Furthermore, the analysis reveals key characteristics of these clinical narratives, including the predominance of nominal events and the prevalence of simultaneity as the most frequent temporal relation type.",{"paper_id":3388,"title":3389,"year":7,"month":358,"day":135,"doi":3390,"resource_url":3391,"first_page":3392,"last_page":3393,"pdf_url":3394,"poster_url":135,"slide_url":135,"video_url":135,"supplementary_url":135,"bibkey":3395,"paper_type":2658,"authors":3396,"abstract":3422},"lrec2026-ws-clinicalnlp-35","MOSAIC: A Multilingual, Taxonomy-Agnostic, and Computationally Efficient Approach for Radiological Report Classification in Low-Resource Settings ","10.63317\u002F3qvne99cm779","https:\u002F\u002Flrec.elra.info\u002Flrec2026-ws-clinicalnlp-35","313","323","http:\u002F\u002Fwww.lrec-conf.org\u002Fproceedings\u002Flrec2026\u002Fworkshops\u002Fclinicalnlp\u002Fpdf\u002F2026.clinicalnlp-1.35.pdf","schiavone-etal-2026-mosaic",[3397,3400,3402,3405,3408,3411,3414,3416,3419],{"paper_id":3388,"author_seq":459,"given_name":3398,"surname":3399,"affiliation":135,"orcid":135},"Alice","Schiavone",{"paper_id":3388,"author_seq":434,"given_name":1433,"surname":3401,"affiliation":135,"orcid":135},"Fraccaro",{"paper_id":3388,"author_seq":408,"given_name":3403,"surname":3404,"affiliation":135,"orcid":135},"Lea Marie","Pehrson",{"paper_id":3388,"author_seq":387,"given_name":3406,"surname":3407,"affiliation":135,"orcid":135},"Silvia","Ingala",{"paper_id":3388,"author_seq":358,"given_name":3409,"surname":3410,"affiliation":135,"orcid":135},"Rasmus","Bonnevie",{"paper_id":3388,"author_seq":333,"given_name":3412,"surname":3413,"affiliation":135,"orcid":135},"Michael Bachmann","Nielsen",{"paper_id":3388,"author_seq":309,"given_name":2404,"surname":3415,"affiliation":135,"orcid":135},"Beliveau",{"paper_id":3388,"author_seq":280,"given_name":3417,"surname":3418,"affiliation":135,"orcid":135},"Melanie","Ganz",{"paper_id":3388,"author_seq":252,"given_name":3420,"surname":3421,"affiliation":135,"orcid":135},"Desmond","Elliott","Radiology reports contain rich clinical information that can be used to train imaging models without relying on costly manual annotation. However, existing approaches face critical limitations: rule-based methods struggle with linguistic variability, supervised models require large annotated datasets, and recent LLM-based systems depend on closed-source or resource-intensive models that are unsuitable for clinical use. Moreover, current solutions are largely restricted to English and single-modality, single-taxonomy datasets. We introduce MOSAIC, a multilingual, taxonomy-agnostic, and computationally efficient approach for radiological report classification. Built on a compact open-access language model (MedGemma-4B), MOSAIC supports both zero-\u002Ffew-shot prompting and lightweight fine-tuning, enabling deployment on consumer-grade GPUs. We evaluate MOSAIC across seven datasets in English, Spanish, French, and Danish, spanning multiple imaging modalities and label taxonomies. The model achieves a mean macro F1 score of 88 across five chest X-ray datasets, approaching or exceeding expert-level performance, while requiring only 24 GB of GPU memory. With data augmentation, as few as 80 annotated samples are sufficient to reach a weighted F1 score of 82 on Danish reports, enabling large-scale cohort classification with minimal human effort. Code and models are open-source, offering a practical alternative to large or proprietary LLMs in clinical settings.",{"paper_id":3424,"title":3425,"year":7,"month":358,"day":135,"doi":3426,"resource_url":3427,"first_page":3428,"last_page":3429,"pdf_url":3430,"poster_url":135,"slide_url":135,"video_url":135,"supplementary_url":135,"bibkey":3431,"paper_type":2658,"authors":3432,"abstract":3443},"lrec2026-ws-clinicalnlp-36","MedNormJ: A Benchmark Dataset for Medical Concept Normalization in Japanese Clinical Documents ","10.63317\u002F3tc32wmofkbm","https:\u002F\u002Flrec.elra.info\u002Flrec2026-ws-clinicalnlp-36","324","335","http:\u002F\u002Fwww.lrec-conf.org\u002Fproceedings\u002Flrec2026\u002Fworkshops\u002Fclinicalnlp\u002Fpdf\u002F2026.clinicalnlp-1.36.pdf","tashiro-etal-2026-mednormj",[3433,3436,3438,3441,3442],{"paper_id":3424,"author_seq":459,"given_name":3434,"surname":3435,"affiliation":135,"orcid":135},"Yuki","Tashiro",{"paper_id":3424,"author_seq":434,"given_name":3169,"surname":3437,"affiliation":135,"orcid":135},"Shimizu",{"paper_id":3424,"author_seq":408,"given_name":3439,"surname":3440,"affiliation":135,"orcid":135},"Tomohiro","Nishiyama",{"paper_id":3424,"author_seq":387,"given_name":3175,"surname":3176,"affiliation":135,"orcid":135},{"paper_id":3424,"author_seq":358,"given_name":3178,"surname":3179,"affiliation":135,"orcid":135},"Medical concept normalization in clinical text is a fundamental technology for the secondary use of clinical data. However, constructing annotated resources for this task is challenging because annotation is both expertise-intensive and methodologically complex. As a result, a standard evaluation dataset for Japanese has yet to be established. In this study, we introduce a Japanese dataset for medical concept normalization, MedNormJ, which will be publicly available. The dataset consists of 397 pairs of medical expressions and their corresponding normalized disease names, manually curated from 96 medical documents, including case reports and radiology reports. Furthermore, we conduct comparative experiments using existing normalization approaches to benchmark their performance on this dataset in terms of both accuracy and computational efficiency. Through these experiments, we clarify the present performance level and identify remaining challenges specific to Japanese medical concept normalization.",{"paper_id":3445,"title":3446,"year":7,"month":358,"day":135,"doi":3447,"resource_url":3448,"first_page":3449,"last_page":3450,"pdf_url":3451,"poster_url":135,"slide_url":135,"video_url":135,"supplementary_url":135,"bibkey":3452,"paper_type":2658,"authors":3453,"abstract":3475},"lrec2026-ws-clinicalnlp-37","Pediatric Sepsis Cohort Detection Using In-Context Pointwise V-Usable Information ","10.63317\u002F38bj4pwcnt3q","https:\u002F\u002Flrec.elra.info\u002Flrec2026-ws-clinicalnlp-37","336","349","http:\u002F\u002Fwww.lrec-conf.org\u002Fproceedings\u002Flrec2026\u002Fworkshops\u002Fclinicalnlp\u002Fpdf\u002F2026.clinicalnlp-1.37.pdf","li-etal-2026-pediatric",[3454,3456,3459,3460,3463,3466,3469,3472],{"paper_id":3445,"author_seq":459,"given_name":3455,"surname":1290,"affiliation":135,"orcid":135},"Yingya",{"paper_id":3445,"author_seq":434,"given_name":3457,"surname":3458,"affiliation":135,"orcid":135},"Alon","Geva",{"paper_id":3445,"author_seq":408,"given_name":1913,"surname":1914,"affiliation":135,"orcid":135},{"paper_id":3445,"author_seq":387,"given_name":3461,"surname":3462,"affiliation":135,"orcid":135},"Timothy A.","Miller",{"paper_id":3445,"author_seq":358,"given_name":3464,"surname":3465,"affiliation":135,"orcid":135},"Kate","Madden",{"paper_id":3445,"author_seq":333,"given_name":3467,"surname":3468,"affiliation":135,"orcid":135},"Matthew A.","Eisenberg",{"paper_id":3445,"author_seq":309,"given_name":3470,"surname":3471,"affiliation":135,"orcid":135},"Daniel P.","Kelly",{"paper_id":3445,"author_seq":280,"given_name":3473,"surname":3474,"affiliation":135,"orcid":135},"Guergana","Savova","Pediatric sepsis diagnosis remains a major clinical challenge due to non-specific symptoms and a lack of reliable diagnostic criteria. Large language models (LLMs) provide a scalable solution for processing and understanding unstructured text in medical records. However, identifying the most suitable model is non-trivial given the rapid growth of available LLMs. In this work, we proposed using in-context pointwise V-usable information (pvi) to estimate task difficulty and guide model selection for pediatric sepsis cohort detection. We applied in-context pvi to estimate task difficulty and inform model selection across 12 state-of-the-art open LLMs on the task, using electronic medical record data from 507 patient encounters at a U.S. children’s hospital. We compared the performance of the best-fitting LLM to feature-rich baseline models and a fine-tuned transformer. Our results show that the pvi-selected LLM outperforms the baselines, although the feature-rich bag-of-words model with a support vector machine also achieves competitive performance. We believe our approach demonstrates a promising application of current LLM techniques to high-stakes clinical tasks.",{"paper_id":3477,"title":3478,"year":7,"month":358,"day":135,"doi":3479,"resource_url":3480,"first_page":3481,"last_page":3482,"pdf_url":3483,"poster_url":135,"slide_url":135,"video_url":135,"supplementary_url":135,"bibkey":3484,"paper_type":2658,"authors":3485,"abstract":3504},"lrec2026-ws-clinicalnlp-38","RECAP: Transparent Inference-Time Emotion Alignment for Medical Dialogue Systems ","10.63317\u002F5epg4xxjjygt","https:\u002F\u002Flrec.elra.info\u002Flrec2026-ws-clinicalnlp-38","350","368","http:\u002F\u002Fwww.lrec-conf.org\u002Fproceedings\u002Flrec2026\u002Fworkshops\u002Fclinicalnlp\u002Fpdf\u002F2026.clinicalnlp-1.38.pdf","srinivasan-etal-2026-recap",[3486,3489,3492,3495,3498,3501],{"paper_id":3477,"author_seq":459,"given_name":3487,"surname":3488,"affiliation":135,"orcid":135},"Adarsh","Srinivasan",{"paper_id":3477,"author_seq":434,"given_name":3490,"surname":3491,"affiliation":135,"orcid":135},"Jacob","Dineen",{"paper_id":3477,"author_seq":408,"given_name":3493,"surname":3494,"affiliation":135,"orcid":135},"Muhammad Uzair","Sarfraz",{"paper_id":3477,"author_seq":387,"given_name":3496,"surname":3497,"affiliation":135,"orcid":135},"Muhammad Umar","Afzal",{"paper_id":3477,"author_seq":358,"given_name":3499,"surname":3500,"affiliation":135,"orcid":135},"Irbaz","Riaz",{"paper_id":3477,"author_seq":333,"given_name":3502,"surname":3503,"affiliation":135,"orcid":135},"Ben","Zhou","Large language models in healthcare often produce emotionally flat or opaque responses, failing to provide the transparent reasoning required for clinical trust. We present RECAP (Reflect–Extract–Calibrate–Align–Produce), an inference-time framework grounded in cognitive appraisal theory that decomposes patient input into auditable, appraisal-theoretic stages without retraining. Across multiple benchmarks and models from 8B to 120B parameters, RECAP improves alignment with human judgments, with gains inversely proportional to model scale. Intermediate outputs further reveal that models systematically underweight relational factors such as social support. In blinded evaluations, oncology fellows rated RECAP responses significantly higher than baselines with 76–88% win rates, demonstrating that principled prompting can enhance medical AI’s emotional intelligence while maintaining the transparency required for clinical deployment.",{"paper_id":3506,"title":3507,"year":7,"month":358,"day":135,"doi":3508,"resource_url":3509,"first_page":3510,"last_page":3511,"pdf_url":3512,"poster_url":135,"slide_url":135,"video_url":135,"supplementary_url":135,"bibkey":3513,"paper_type":2658,"authors":3514,"abstract":3521},"lrec2026-ws-clinicalnlp-39","Disentangling Ambiguity from Instability in Large Language Models: A Clinical Text-to-SQL Case Study ","10.63317\u002F4qse3ioqyh8s","https:\u002F\u002Flrec.elra.info\u002Flrec2026-ws-clinicalnlp-39","369","380","http:\u002F\u002Fwww.lrec-conf.org\u002Fproceedings\u002Flrec2026\u002Fworkshops\u002Fclinicalnlp\u002Fpdf\u002F2026.clinicalnlp-1.39.pdf","ziletti-etal-2026-disentangling",[3515,3518],{"paper_id":3506,"author_seq":459,"given_name":3516,"surname":3517,"affiliation":135,"orcid":135},"Angelo","Ziletti",{"paper_id":3506,"author_seq":434,"given_name":3519,"surname":3520,"affiliation":135,"orcid":135},"Leonardo","D'Ambrosi","Deploying large language models for clinical Text-to-SQL requires distinguishing two qualitatively different causes of output diversity: (i) input ambiguity that should trigger clarification, and (ii) model instability that should trigger human review. We propose CLUES, a framework that models Text-to-SQL as a two-stage process (interpretations –> answers) and decomposes semantic uncertainty into an ambiguity score and an instability score. The instability score is computed via the Schur complement of a bipartite semantic graph matrix. Across AmbigQA\u002FSituatedQA (gold interpretations) and a clinical Text-to-SQL benchmark (known interpretations), CLUES improves failure prediction over state-of-the-art Kernel Language Entropy. In deployment settings, it remains competitive while providing a diagnostic decomposition unavailable from a single score. The resulting uncertainty regimes map to targeted interventions - query refinement for ambiguity, model improvement for instability. The high-ambiguity\u002Fhigh-instability regime contains 51% of errors while covering 25% of queries, enabling efficient triage.",{"paper_id":3523,"title":3524,"year":7,"month":358,"day":135,"doi":3525,"resource_url":3526,"first_page":3527,"last_page":3528,"pdf_url":3529,"poster_url":135,"slide_url":135,"video_url":135,"supplementary_url":135,"bibkey":3530,"paper_type":2658,"authors":3531,"abstract":3543},"lrec2026-ws-clinicalnlp-40","An OMOP-Based Open-Source Text-to-SQL Benchmark Dataset ","10.63317\u002F3hfefjmhhymh","https:\u002F\u002Flrec.elra.info\u002Flrec2026-ws-clinicalnlp-40","381","393","http:\u002F\u002Fwww.lrec-conf.org\u002Fproceedings\u002Flrec2026\u002Fworkshops\u002Fclinicalnlp\u002Fpdf\u002F2026.clinicalnlp-1.40.pdf","legrand-etal-2026-omop",[3532,3534,3537,3540],{"paper_id":3523,"author_seq":459,"given_name":1111,"surname":3533,"affiliation":135,"orcid":135},"Legrand",{"paper_id":3523,"author_seq":434,"given_name":3535,"surname":3536,"affiliation":135,"orcid":135},"Kawsar","Noor",{"paper_id":3523,"author_seq":408,"given_name":3538,"surname":3539,"affiliation":135,"orcid":135},"Satyam","Bhagwanani",{"paper_id":3523,"author_seq":387,"given_name":3541,"surname":3542,"affiliation":135,"orcid":135},"Richard J.","Dobson","Access to electronic health record (EHR) warehouses is limited by SQL expertise and complex clinical schemas. We present an open-source OMOP Common Data Model text-to-SQL benchmark (CDM v5.4) with a safety contract: output one executable SQL statement or the abstention token (\u003CNO_SQL>) for unanswerable requests. Inputs are concept-normalized (entities as OMOP concept IDs) to decouple SQL generation from entity linking. We evaluate by executing predicted and reference queries on a synthetic OMOP PostgreSQL database, reporting Execution Accuracy (result equivalence) and a reliability score that rewards correct abstention and penalizes unsafe attempts. The dataset includes 6,690 paraphrases from 75 OMOP-adapted templates with leakage-resistant template\u002FSQL-variation splits. LoRA-tuned Llama-3-8B-Instruct achieves 93.55% execution accuracy with improved abstention reliability, while schema-injected baselines fail the contract. We release the dataset, splits, database dump, and a reproducible evaluation pipeline to support reliable clinical analytics assistants.",{"paper_id":3545,"title":3546,"year":7,"month":358,"day":135,"doi":3547,"resource_url":3548,"first_page":3549,"last_page":3550,"pdf_url":3551,"poster_url":135,"slide_url":135,"video_url":135,"supplementary_url":135,"bibkey":3552,"paper_type":2658,"authors":3553,"abstract":3574},"lrec2026-ws-clinicalnlp-41","Profiling Hallucinations in Frontier LLMs for Entity Linking to Medical Ontologies ","10.63317\u002F4zi4vcu7vz4v","https:\u002F\u002Flrec.elra.info\u002Flrec2026-ws-clinicalnlp-41","394","413","http:\u002F\u002Fwww.lrec-conf.org\u002Fproceedings\u002Flrec2026\u002Fworkshops\u002Fclinicalnlp\u002Fpdf\u002F2026.clinicalnlp-1.41.pdf","born-etal-2026-profiling",[3554,3557,3559,3562,3565,3568,3571],{"paper_id":3545,"author_seq":459,"given_name":3555,"surname":3556,"affiliation":135,"orcid":135},"Logan","Born",{"paper_id":3545,"author_seq":434,"given_name":2935,"surname":3558,"affiliation":135,"orcid":135},"Kambhatla",{"paper_id":3545,"author_seq":408,"given_name":3560,"surname":3561,"affiliation":135,"orcid":135},"Uliyana","Kubasova",{"paper_id":3545,"author_seq":387,"given_name":3563,"surname":3564,"affiliation":135,"orcid":135},"Maryam","Siahbani",{"paper_id":3545,"author_seq":358,"given_name":3566,"surname":3567,"affiliation":135,"orcid":135},"Andrei","Vacariu",{"paper_id":3545,"author_seq":333,"given_name":3569,"surname":3570,"affiliation":135,"orcid":135},"Timothy W.","O'Connell",{"paper_id":3545,"author_seq":309,"given_name":3572,"surname":3573,"affiliation":135,"orcid":135},"Anoop","Sarkar","The integration of Large Language Models (LLMs) into healthcare promises to revolutionize clinical documentation and interoperability, yet reliability remains a concern. This study presents a comprehensive analysis of hallucinations by frontier LLMs tasked with mapping clinical text to SNOMED CT. Through rigorous experimentation, we identify a critical reliability gap: LLMs hallucinate medical codes at a rate that currently renders them unsuitable for autonomous clinical coding applications. Paradoxically, constraining models to use ground-truth mention spans exacerbates, rather than mitigates, these hallucinations. We further contribute a taxonomy of hallucination types – including deprecated codes and cross-ontology errors – and demonstrate that general-purpose LLMs significantly underperform compared to specialized zero-shot entity linking approaches. These findings underscore the need for robust verification mechanisms before clinical deployment."]