Clinical Language Model Evaluation

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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.

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

Reliability Auditing for Downstream LLM tasks in Psychiatry: LLM-Generated Hospitalization Risk Scores
Apr 23, 2026Shevya Panda, Shinjini Bose, Ananya JoshiPrompt Sensitivity LLM Auditing
Johns Hopkins University Baltimore, USA
Evaluating Patient Safety Risks in Generative AI: Development and Validation of a FMECA Framework for Generated Clinical Content
Apr 23, 2026Lydie Bednarczyk, Jamil Zaghir, Julien Ehrsam +11Clinical Language Model Evaluation Medical LLMs
Division of Medical Information Sciences, Diagnostic Department, Geneva University Hospitals (HUG), Geneva, Switzerland. · Department of Radiology and Medical Informatics, Faculty of Medicine, University of Geneva, Geneva, Switzerland. · Pharmacy, Geneva University Hospital (HUG), and Institute of Pharmaceutical Sciences of Western Switzerland, School of Pharmaceutical Sciences, University of Geneva, Geneva, Switzerland. +3
When Correct Beliefs Collapse: Epistemic Resilience of LLMs under Clinical Pressure
Apr 23, 2026Boyu Xiao, Xiuqi Tian, Xuwen Song +4LLM Sycophancy Healthcare
Research Center for Social Computing and Interactive Robotics, Harbin Institute of Technology, China
Serialisation Strategy Matters: How FHIR Data Format Affects LLM Medication Reconciliation
Apr 22, 2026Sanjoy PatorHealthcare LLM Information Extraction
Independent Researcher
Can "AI" Be a Doctor? A Study of Empathy, Readability, and Alignment in Clinical LLMs
Apr 22, 2026Mariano Barone, Francesco Di Serio, Roberto Moio +4Healthcare LLM Prompting
Department of Electrical Engineering and Information Technology, University of Naples Federico II, Via Claudio 21, Naples, 80125, Italy. · Department of Translational Medical Sciences, University of Campania ”Luigi Vanvitelli”, Via Leonardo Bianchi, Naples, 80131, Italy. · Department of Computer Science, McCormick School of Engineering and Applied Science, Northwestern University, 2309 Sheridan Rd, Evanston, 60201, IL, United States.
Graph2Counsel: Clinically Grounded Synthetic Counseling Dialogue Generation from Client Psychological Graphs
Apr 22, 2026Aishik Mandal, Hiba Arnaout, Clarissa W. Ong +5Mental Health Counseling Mental Health
UKP Lab, Department of Computer Science and Hessian Center for AI (hessian.AI), Technische Universität Darmstadt · Zuse School · National Research Center for Applied Cybersecurity ATHENE +4
Cross-Model Consistency of AI-Generated Exercise Prescriptions: A Repeated Generation Study Across Three Large Language Models
Apr 21, 2026Kihyuk LeeLLM Evaluation Clinical Language Model Evaluation
Data Convergence Team, Office of Hospital Information, Seoul National University Bundang Hospital, Seongnam, Republic of Korea
MedProbeBench: Systematic Benchmarking at Deep Evidence Integration for Expert-level Medical Guideline
Apr 20, 2026Jiyao Liu, Jianghan Shen, Sida Song +19Healthcare AI Agent Benchmarks
Fudan University, Shanghai, China · Shanghai Artificial Intelligence Laboratory, Shanghai, China · Nanjing University, Nanjing, China +1
First, Do No Harm (With LLMs): Mitigating Racial Bias via Agentic Workflows
Apr 20, 2026Sihao Xing, Zaur GoulievHealthcare Social Bias in Language Models
School of Enterprise Computing and Digital Transformation Technological University Dublin · School of Information and Communication Studies University College Dublin
MHSafeEval: Role-Aware Interaction-Level Evaluation of Mental Health Safety in Large Language Models
Apr 20, 2026Suhyun Lee, Palakorn Achananuparp, Neemesh Yadav +2Mental Health Counseling LLM Evaluation
Department of Artificial Intelligence, Hanyang University, Seoul, Republic of Korea · School of Computing and Information Systems, Singapore Management University, Singapore
PsychBench: Auditing Epidemiological Fidelity in Large Language Model Mental Health Simulations
Apr 19, 2026Patrick KeoughHealthcare LLM Auditing
Independent Researcher Bremerton, Washington, USA
Calibrated? Not for Everyone: How Sexual Orientation and Religious Markers Distort LLM Accuracy and Confidence in Medical QA
Apr 19, 2026Alberto Testoni, Iacer CalixtoIntersectional Bias in Language Models Healthcare
Department of Medical Informatics, Amsterdam University Medical Center, University of Amsterdam, Amsterdam, The Netherlands. · Amsterdam Public Health, Methodology, Amsterdam, The Netherlands.
MedPRMBench: A Fine-grained Benchmark for Process Reward Models in Medical Reasoning
Apr 19, 2026Xiang Zheng, Lingyan Wu, Weiqi Zhai +4Healthcare Process Reward Models
Alibaba Group, China
Evaluating Multimodal LLMs for Inpatient Diagnosis: Real-World Performance, Safety, and Cost Across Ten Frontier Models
Apr 18, 2026Bruce A. Bassett, Amy Rouillard, Sitwala Mundia +8Language Model Safety Evaluation Healthcare
School of Computer Science and Applied Mathematics, University of the Witwatersrand, Johannesburg, South Africa · Wits MIND Institute, University of the Witwatersrand, Johannesburg, South Africa · Grai Labs, Cape Town, South Africa +3
HealthCraft: A Reinforcement Learning Safety Environment for Emergency Medicine
Apr 18, 2026Brandon DentHealthcare AI Agent Benchmarks
GOATnote Inc.
Blinded Multi-Rater Comparative Evaluation of a Large Language Model and Clinician-Authored Responses in CGM-Informed Diabetes Counseling
Apr 16, 2026Zhijun Guo, Alvina Lai, Emmanouil Korakas +6Clinical Decision Support RAG Evaluation
Institute of Health Informatics, University College London, London, United Kingdom · University College London Hospitals NHS Foundation Trust, London, United Kingdom · Dartford and Gravesham NHS Foundation Trust, Dartford, United Kingdom +1
Can LLMs Accurately Score Medical Diagnoses and Clinical Reasoning?
Apr 16, 2026Amy Rouillard, Sitwala Mundia, Linda Camara +8LLM-as-a-Judge Healthcare
aWits MIND Institute, University of the Witwatersrand, Johannesburg, South Africa · fSchool of Computer Science and Applied Mathematics, University of the Witwatersrand, Johannesburg, South Africa · bGrai Labs, Cape Town, South Africa +3
Beyond Literal Summarization: Redefining Hallucination for Medical SOAP Note Evaluation
Apr 16, 2026Bhavik Vachhani, Kush Shrisvastava, Pranshu Nema +1Medical Report Generation Clinical Reasoning
Augnito Research
Continued Pretraining of FinBERT on Finnish Histopathological Reports: Train-Time Signals and Proxy Downstream Correlations
Apr 16, 2026Rami Luisto, Liisa Petäinen, Tommi Grönholm +5Healthcare Domain-Adaptive Pretraining
Faculty of Information Technology, University of Jyväskylä, Jyväskylä, Finland. · Digital Workforce Services, Helsinki, Finland. · Heart and Lung Center, Helsinki University Hospital, Helsinki, Finland. +2
A Multi-Domain Red Teaming Framework for Safety, Robustness, and Fairness Evaluation of Medical Large Language Models
Apr 15, 2026Andrei Marian Feier, Veysel Kocaman, Yigit Gul +6LLM Safety LLM Safety Evaluation
John Snow Labs Inc.
When AI Tells You What You Want to Hear: Sycophantic Behavior of Large Language Models in Dementia Care Settings
Apr 13, 2026Christian KolbDementia Care LLM Sycophancy
Consistency of AI-Generated Exercise Prescriptions: A Repeated Generation Study Using a Large Language Model
Apr 13, 2026Kihyuk LeeSelf-Consistency in Language Models Medical LLMs
Data Convergence Team, Office of Hospital Information, Seoul National University Bundang Hospital, Seongnam, Republic of Korea
How Robust Are Large Language Models for Clinical Numeracy? An Empirical Study on Numerical Reasoning Abilities in Clinical Contexts
Apr 13, 2026Minh-Vuong Nguyen, Fatemeh Shiri, Zhuang Li +1Numerical Reasoning in Language Models Clinical Language Model Evaluation
School of Computing Technologies, RMIT University, Australia
Evaluating Small Open LLMs for Medical Question Answering: A Practical Framework
Apr 12, 2026Avi-ad Avraam BuskilaLLM Evaluation LLM Reliability
Department of Information Science and Applied Artificial Intelligence, Bar-Ilan University, Ramat-Gan, Israel
VeriSim: A Configurable Framework for Stress-Testing Medical AI Under Patient Communication Noise
Apr 12, 2026Sina Mansouri, Mohit Marvania, Vibhavari Ashok Shihorkar +5Healthcare LLM Reliability
Department of Computer Science, George Mason University · Department of Health Administration and Policy, College of Public Health, George Mason University · Department of Science, George Mason University
Comparative Analysis of Large Language Models in Healthcare
Apr 11, 2026Subin Santhosh, Farwa Abbas, Hussain Ahmad +1Medical LLMs Clinical Language Model Evaluation
Adelaide University, Australia · South Australian Health and Medical Research Institute, Australia
Do LLMs Reliably Identify Correct Information Units in Aphasic Discourse?
Apr 10, 2026Jason M Pittman, Yesenia Medina-Santos, Anton Phillips +1Clinical Language Model Evaluation
Department of Speech, Language and Hearing Sciences · Program in Neuroscience
MedConceal: A Benchmark for Clinical Hidden-Concern Reasoning Under Partial Observability
Apr 9, 2026Yikun Han, Joey Chan, Jingyuan Chen +3Clinical Language Model Evaluation Clinical Reasoning
University of Illinois Urbana-Champaign · NYC Health + Hospitals/Jacobi
ProMedical: Hierarchical Fine-Grained Criteria Modeling for Medical LLM Alignment via Explicit Injection
Apr 9, 2026He Geng, Yangmin Huang, Lixian Lai +5LLM Alignment Medical LLMs
Xunfei Healthcare Technology Co., Ltd.
LLM-Based Data Generation and Clinical Skills Evaluation for Low-Resource French OSCEs
Apr 9, 2026Tian Huang, Tom Bourgeade, Irina IllinaEducational Assessment Clinical Language Model Evaluation
Université de Lorraine, CNRS, Inria, LORIA, F-54000 Nancy, France

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Reliability Auditing for Downstream LLM tasks in Psychiatry: LLM-Generated Hospitalization Risk Scores

Shevya Panda, Shinjini Bose, Ananya Joshi

Johns Hopkins University Baltimore, USA

Prompt Sensitivity LLM Auditing Mental Health Clinical Language Model Evaluation Clinical Risk Prediction

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Large language models (LLMs) are increasingly utilized in clinical reasoning and risk assessment. However, their interpretive reliability in critical and indeterminate domains such as psychiatry remains unclear. Prior work has identified algorithmic biases and prompt sensitivity in these systems, raising concerns about how contextual information may influence model outputs, but there remains no systematic way to assess these, especially in the psychiatric domain. We propose an approach for reliability auditing downstream LLM tasks by structuring evaluation around the impact of prompt design and the inclusion of medically insignificant inputs on predicted hospitalization risk scores, which is often the first downstream AI clinical-decision-making task. In our audit, a cohort of synthetic patient profiles (n = 50) is generated, each consisting of 15 clinically relevant features and up to 50 clinically insignificant features, across four prompt reframings (neutral, logical, human impact, clinical judgment). We audit four LLMs (Gemini 2.5 Flash, LLaMa 3.3 70b, Claude Sonnet 4.6, GPT-4o mini), and our results show that including medically insignificant variables resulted in a statistically significant increase in the absolute mean predicted hospitalization risk and output variability across all models and prompts, indicating reduced predictive stability as contextual noise increased. Clinically insignificant features had an effect on instability across many model-prompt conditions, and prompt variations independently affected the trajectory of instability in a model-dependent manner. These findings quantify how LLM-based psychiatric risk assessments are sensitive to non-clinical information, highlighting the need for systematic evaluations of attributional stability and uncertainty behavior like this before clinical deployments.