Clinical Language Model Evaluation

Follow this topic Ask about this topic

Clinical language model evaluation asks whether models can answer medical questions, interpret records, support conversations, and assist with decisions without causing harm. Researchers use exam-style benchmarks, clinician-authored rubrics, electronic health records, simulated consultations, and tests that vary language, missing information, patient behavior, or evidence; they also compare expert ratings with automated judges. Evaluation increasingly measures safety, omissions, evidence grounding, uncertainty, consistency, and workflow performance alongside accuracy. A central challenge is that strong scores on static tests or plausible reasoning may not transfer to dynamic, incomplete clinical encounters, while automated judges can disagree with clinicians and require validation.

Latest papers

419 papers49 papers in the last four weeks, up 2% on the four weeks before. 0.3% of all new papers.

Can We Trust LLMs for Mental Health Screening? Consistency, ASR Robustness, and Evidence Faithfulness
May 10, 2026Erfan Loweimi, Sofia de la Fuente Garcia, Samira Loveymi +2Healthcare Mental Health
Usher Institute, Edinburgh Medical School, University of Edinburgh, Edinburgh, UK · Department of Computer Engineering, Ahvaz Campus, Islamic Azad University, Ahvaz, Iran · School of Health and Social Care, Edinburgh Napier University, Edinburgh, UK
CLR-voyance: Reinforcing Open-Ended Reasoning for Inpatient Clinical Decision Support with Outcome-Aware Rubrics
May 10, 2026Aishik Nagar, Arun-Kumar Kaliya-Perumal, Yu-Hsuan Han +5Healthcare Clinical Decision Support
ASUS Intelligent Cloud Services (AICS) · Rehabilitation Research Institute of Singapore, Nanyang Technological University · Department of Family Medicine,2026 Taipei Veterans General Hospital +2
Can Revealed Preferences Clarify LLM Alignment and Steering?
May 8, 2026Khurram Yamin, Jingjing Tang, Eric Horvitz +1Language Model Steering LLM Decision-Making
1Carnegie Mellon University · 2Microsoft Research
Can Language Models Identify Side Effects of Breast Cancer Radiation Treatments?
May 8, 2026Natalie Seah, Danielle S. Bitterman, Daphna Spiegel +1Breast Cancer Healthcare
University of Virginia · Mass General Brigham, Dana-Farber Cancer Institute, Harvard Medical School
Automated Clinical Report Generation for Remote Cognitive Remediation: Comparing Knowledge-Engineered Templates and LLMs in Low-Resource Settings
May 7, 2026Yongxin Zhou, Fabien Ringeval, François PortetMedical Report Generation Clinical Language Model Evaluation
Univ. Grenoble Alpes, CNRS, Grenoble INP, LIG, 38000 Grenoble, France
Systematic Evaluation of Large Language Models for Post-Discharge Clinical Action Extraction
May 7, 2026Shivali Dalmia, Ananya Mantravadi, Prasanna DesikanDocument Information Extraction Clinical Information Extraction
Centific AI Research · Centific Global Solutions, Inc.
TheraAgent: Self-Improving Therapeutic Agent for Precise and Comprehensive Treatment Planning
May 7, 2026Junkai Li, Yunghwei Lai, Tianyi Zhu +3LLM Self-Refinement Healthcare
1Dept. of Comp. Sci. & Tech., Institute for AI, Tsinghua University, Beijing, China · Institute for AI Industry Research (AIR), Tsinghua University, Beijing, China · School of Computing, National University of Singapore, Singapore
Ask Before You Diagnose: Safe-Psych, a Sequential Evaluation Benchmark for LLMs in Psychiatry
May 6, 2026Oriana Presacan, Andreea Grama, Larisa Irimină +6Selective Prediction Healthcare
National University of Science and Technology Politehnica Bucharest · Psychiatric Hospital Doctor Gheorghe Preda · Oslo Metropolitan University +3
Self-Prompting Small Language Models for Privacy-Sensitive Clinical Information Extraction
May 5, 2026Yao-Shun Chuang, Tushti Mody, Uday Pratap Singh +6Named Entity Recognition Healthcare
McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, United States · School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX 77030, United States · School of Dentistry, The University of Texas Health Science Center at Houston, Houston, TX 77054, United States +1
Safety and accuracy follow different scaling laws in clinical large language models
May 5, 2026Sebastian Wind, Tri-Thien Nguyen, Jeta Sopa +9LLM Safety Benchmarks Retrieval-Augmented Generation
Pattern Recognition Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany · Institute of Radiology, University Hospital Erlangen, Erlangen, Germany · Lab for AI in Medicine, RWTH Aachen University, Aachen, Germany +3
EQUITRIAGE: A Fairness Audit of Gender Bias in LLM-Based Emergency Department Triage
May 5, 2026Richard J. Young, Alice M. MatthewsGender Bias in Language Models Clinical Triage
University of Nevada, Las Vegas, Lee Business School, Las Vegas, NV, USA · DeepNeuro AI, Las Vegas, NV, USA · Concorde Career Colleges, Dept. of Cardiovascular and Medical Diagnostic Sonography
Atomic Fact-Checking Increases Clinician Trust in Large Language Model Recommendations for Oncology Decision Support: A Randomized Controlled Trial
May 5, 2026Lisa C. Adams, Linus Marx, Erik Thiele Orberg +8Healthcare Clinical Decision Support
Department of Diagnostic and Interventional Radiology, TUM University Hospital Rechts der Isar, TUM School of Medicine and Health, Technical University of Munich, Munich, Germany · Department of Radiation Oncology, TUM University Hospital Rechts der Isar, TUM School of Medicine and Health, Technical University of Munich, Munich, Germany · Department of Internal Medicine III: Hematology and Clinical Oncology, University Hospital Regensburg, University of Regensburg, Regensburg, Germany +4
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
Auditing Stealth Sycophancy in Mental-Health Dialogue: Structured Clinical-State Diagnostics and Clean Matched Benchmarks
May 5, 2026Tianze Han, Beining Xu, Hanbo Zhang +1Mental Health Counseling LLM Sycophancy
Shenzhen MSU-BIT University, Shenzhen, China
ADAPTS: Agentic Decomposition for Automated Protocol-agnostic Tracking of Symptoms
May 4, 2026Alexandria K. Vail, Marcelo Cicconet, Katie Aafjes-van Doorn +2Depression Detection Mental Health
Deliberate Solutions, Inc. New York, New York, USA
Reliability-Oriented Multilingual Orthopedic Diagnosis: A Domain-Adaptive Modeling and a Conceptual Validation Framework
May 4, 2026Danish Ali, Li Xiaojian, Sundas Iqbal +1Multilingual Language Model Evaluation Healthcare
School of Computer Science, Wuhan University, China · School of Software, Nanjing University of Information Science and Technology (NUIST), China · Department of Orthopedic, Bahawal Victoria Hospital, Pakistan
PhysicianBench: Evaluating LLM Agents in Real-World EHR Environments
May 4, 2026Ruoqi Liu, Imran Q. Mohiuddin, Austin J. Schoeffler +10AI Agent Benchmarks Electronic Health Records
Stanford University
Artificial intelligence language technologies in multilingual healthcare: Grand challenges ahead
May 2, 2026Vicent Briva-IglesiasMultilingual Language Model Evaluation Language Model Safety Evaluation
School of Applied Languages and Intercultural Studies (SALIS) · CTTS, ADAPT Centre · Dublin City University
Medmarks: A Comprehensive Open-Source LLM Benchmark Suite for Medical Tasks
May 2, 2026Benjamin Warner, Ratna Sagari Grandhi, Max Kieffer +32LLM Evaluation Healthcare
Sophont · MedARC · Prime Intellect
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
CLEAR: Revealing How Noise and Ambiguity Degrade Reliability in LLMs for Medicine
May 1, 2026Kevin H. Guo, Chao Yan, Avinash Baidya +5Healthcare LLM Reliability
Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA · Intuit AI Research, Mountain View, CA, USA · Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA +2
HealthBench Professional: Evaluating Large Language Models on Real Clinician Chats
Apr 30, 2026Rebecca Soskin Hicks, Mikhail Trofimov, Dominick Lim +13Healthcare Clinical Decision Support
OpenAI
Domain-Adapted Small Language Models for Reliable Clinical Triage
Apr 29, 2026Manar Aljohani, Brandon Ho, Kenneth McKinley +2Clinical Triage LLM Fine-Tuning
Department of Computer Science, Virginia Tech, Blacksburg, VA, US · Children’s National Hospital, Washington DC, USA
PSI-Bench: Interpretable and Clinically Meaningful Evaluation of Depression Patient Simulators
Apr 28, 2026Nguyen Khoi Hoang, Shuhaib Mehri, Tse-An Hsu +4Benchmark Design Clinical Language Model Evaluation
University of Illinois Urbana-Champaign · VinUniversity
Single-turn emergency psychiatric triage across 15 frontier AI chatbots
Apr 28, 2026Veith Weilnhammer, Lennart Luettgau, Christopher Summerfield +5Healthcare Clinical Triage
Max Planck UCL Centre for Computational Psychiatry and Ageing Research, London, UK · UK AI Security Institute · Department of Experimental Psychology, University of Oxford, Oxford, UK +2
Analyzing LLM Reasoning to Uncover Mental Health Stigma
Apr 27, 2026Sreehari Sankar, Aliakbar Nafar, Mona Barman +8LLM Evaluation Social Bias in Language Models
BetterHelp
Case-Specific Rubrics for Clinical AI Evaluation: Methodology, Validation, and LLM-Clinician Agreement Across 823 Encounters
Apr 27, 2026Aaryan Shah, Andrew Hines, Alexia Downs +6LLM-as-a-Judge Human-AI Agreement
1Canvas Medical, San Francisco, CA, USA · Department of Biomedical Data Science, Stanford University, Stanford, CA, USA · 3XPC (X Primary Care) +2
Green Shielding: A User-Centric Approach Towards Trustworthy AI
Apr 27, 2026Aaron J. Li, Nicolas Sanchez, Hao Huang +8Prompt Sensitivity Clinical Language Model Evaluation
University of California, Berkeley · University of Melbourne · University of California, San Francisco +2
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
AI Safety Training Can be Clinically Harmful
Apr 25, 2026Suhas BN, Andrew M. Sherrill, Rosa I. Arriaga +2Mental Health Counseling LLM Alignment
College of Information Sciences and Technology, Penn State University, USA · Department of Psychiatry and Behavioral Sciences, Emory University, USA · School of Interactive Computing, Georgia Tech, USA +1

May 10, 2026J/K move · Enter open · S save

Can We Trust LLMs for Mental Health Screening? Consistency, ASR Robustness, and Evidence Faithfulness

Erfan Loweimi, Sofia de la Fuente Garcia, Samira Loveymi, Hadi Daneshvar+1

Usher Institute, Edinburgh Medical School, University of Edinburgh, Edinburgh, UK · Department of Computer Engineering, Ahvaz Campus, Islamic Azad University, Ahvaz, Iran · School of Health and Social Care, Edinburgh Napier University, Edinburgh, UK

Healthcare Mental Health LLM Reliability Depression Severity Estimation Clinical Language Model Evaluation Language Model Robustness+1

Open paper AskPDF arXiv

LLMs can estimate Hospital Anxiety and Depression Scale (HADS) scores from speech in a zero-shot manner, but clinical deployment requires reliability across three dimensions: intra-model consistency, ASR robustness, and evidence faithfulness. We evaluate three LLMs (Phi-4, Gemma-2-9B, and Llama-3.1-8B) on 111 English-speaking participants using ground-truth transcripts and three Whisper ASR variants (Large, Medium, Small), with three independent runs per model-condition pair. We find that (i) Phi-4 and Gemma-2-9B achieve excellent intra-model consistency (ICC > 0.89) with minimal degradation under ASR; (ii) Llama-3.1-8B shows ASR-fragile consistency, with ICC dropping from 0.82 to 0.36 at 10% WER; (iii) predictive validity is largely preserved under ASR for robust models; and (iv) keyword groundedness exceeds 93% for Phi-4 and Gemma-2-9B but falls to 77-81% for Llama-3.1-8B. Inter-model keyword agreement is far lower than score-level agreement, revealing a score-evidence dissociation with implications for clinical interpretability.