Clinical QA

QA: Question Answering

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7 papers in the last four weeks, against 2 the four weeks before. 0.1% of all new papers.

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Key Coverage Matters: Semi-Structured Extraction of OCR Clinical Reports
May 10, 2026Yu Wang, Yingyun Li, Ying Qin +1Healthcare Document Information Extraction
AI Starfish, Hangzhou, China
BIT.UA-AAUBS at ArchEHR-QA 2026: Evaluating Open-Source and Proprietary LLMs via Prompting in Low-Resource QA
May 5, 2026Richard A. A. Jonker, Alexander Christiansen, Alexandros Maniatis +4LLM Grounding Healthcare
Aalborg University Business School Fibigerstræde 2, Aalborg East 9220, Denmark · IEETA, DETI, LASI, University of Aveiro Campus Universitário de Santiago, Aveiro 3810-193, Portugal
Teaching LLMs Brazilian Healthcare: Injecting Knowledge from Official Clinical Guidelines
May 1, 2026Hugo Abonizio, Filipe Rocha Lopes, Roberto Lotufo +1Knowledge Augmentation for Language Models Healthcare
FEEC, UNICAMP, Campinas, Brazil · NoHarm.ai, Brazil · Maritaca AI, Brazil
HealthNLP_Retrievers at ArchEHR-QA 2026: Cascaded LLM Pipeline for Grounded Clinical Question Answering
Apr 29, 2026Md Biplob Hosen, Md Alomgeer Hussein, Md Akmol Masud +3LLM Grounding Healthcare
University of Maryland Baltimore County, Baltimore, MD, USA · 2Jahangirnagar University, Savar, Dhaka, Bangladesh
Agentic clinical reasoning over longitudinal myeloma records: a retrospective evaluation against expert consensus
Apr 27, 2026Johannes Moll, Jannik Lübberstedt, Christoph Nuernbergk +21Healthcare LLM Agent Evaluation
Chair for AI in Healthcare and Medicine, Technical University of Munich (TUM) and TUM University Hospital, Munich, Germany · Department of Diagnostic and Interventional Radiology, Klinikum rechts der Isar, TUM University Hospital, School of Medicine and Health, Technical University of Munich, Munich, Germany · Department of Cardiovascular Radiology and Nuclear Medicine, German Heart Center, TUM University Hospital, School of Medicine and Health, Technical University of Munich, Munich, Germany +11
Quantum Knowledge Graph: Modeling Context-Dependent Triplet Validity
Apr 27, 2026Yao Wang, Zixu Geng, Jun YanKG Construction Knowledge Representation
HKAI-Sci, City University of Hong Kong · Department of Automation, Tsinghua University · Pratt School of Engineering, Duke University
HypEHR: Hyperbolic Modeling of Electronic Health Records for Efficient Question Answering
Apr 22, 2026Yuyu Liu, Sarang Rajendra Patil, Mengjia Xu +1Healthcare Hierarchical Representation Learning
Department of Computer Science, Stony Brook University · Department of Data Science, New Jersey Institute of Technology · Department of Biomedical Informatics, Stony Brook University
Few shot chain-of-thought driven reasoning to prompt LLMs for open ended medical question answering
Mar 7, 2024Saeel Sandeep Nachane, Ojas Gramopadhye, Prateek Chanda +5Healthcare Medical Diagnosis
2Indian Institute of Technology Bombay · 1IBM Research

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Key Coverage Matters: Semi-Structured Extraction of OCR Clinical Reports

Yu Wang, Yingyun Li, Ying Qin, Haiyang Qian

AI Starfish, Hangzhou, China

Healthcare Document Information Extraction Clinical Information Extraction Clinical QA Electronic Health Records

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Clinical reports are often fragmented across healthcare institutions because privacy regulations and data silos limit direct information sharing. When patients seek care at a different hospital, they often carry paper or scanned reports from prior visits. This hinders EHR integration and longitudinal review, and downstream applications that depend on more complete patient records, such as patient management, follow-up care, real-world studies, and clinical-trial matching. Although OCR can digitize such reports, reliable extraction remains challenging because clinical documents are heterogeneous, OCR text is noisy, and many healthcare settings require low-cost on-premise deployment. We formulate this problem as canonical key-conditioned extractive question answering over OCR-derived clinical reports. Because the key fields are neither fixed nor known in advance, the key space is open. We maintain a canonical key inventory through iterative key mining, normalization, clustering, and lightweight human verification, and introduce key coverage as a metric to quantify inventory completeness. Using a 0.2B BERT-based model, experiments on real-world reports from more than 20 hospitals show performance improves monotonically with key coverage. The model achieves F1 scores of 0.839 and 0.893 under exact match and boundary-tolerant matching, respectively, once the Top-90 canonical keys are covered. These results show that key coverage is a dominant factor for end-to-end performance. At Top-90 coverage, our model outperforms a fine-tuned Qwen3-0.6B baseline under exact match. Although our annotated corpus is Chinese, the method relies on the language-agnostic key-value organization of semi-structured clinical reports and can be adapted to other settings given an appropriate canonical key inventory and alias mapping.

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