ArticleNPJ digital medicine2025
A novel evaluation benchmark for medical LLMs illuminating safety and effectiveness in clinical domains.
Article in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
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Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Knowledge-Practice Performance Gap in Clinical Large Language Models: Systematic Review of 39 Benchmarks.Journal of medical Internet research · 2025Pooled it
- The Rise of Small Language Models in Healthcare: A Comprehensive Survey.Computer science review · 2026Article
- Safety-Oriented Benchmarking of Large Language Models in Risk-Based Management of Abnormal Cervical Screening Results: Scenario-Based Benchmark Study.Journal of medical Internet research · 2026Article
- The Reliability of Human Evaluation of Large Language Models in Health Care Settings: Scoping Review.Journal of medical Internet research · 2026Article
- Comparative performance of chatgpt and gemini in diagnostic classification and clinical reasoning for open-angle glaucoma: a standardized scenario-based study.International ophthalmology · 2026Article
- A comprehensive survey of AI agents in healthcare.Journal of biomedical informatics · 2026Review
- In Reply to Sengul I and Sengul D.Advances in radiation oncology · 2026Article
- RESPECT: a conversational AI system for informed consent with accuracy, safety, and stakeholder-centered evaluation.NPJ digital medicine · 2026Article
- Artificial intelligence-powered prediction of diabetic complications: from clinical data to molecular omics.Briefings in bioinformatics · 2026Article
- Deficiencies in clinical reasoning of LLMs in low back pain management and remediation via prompt engineering: from performance evaluation to error diagnosis.Frontiers in artificial intelligence · 2026Article
- A comparative study of the performance of different large language models in the Chinese National Pharmacist Licensing Examination.Frontiers in medicine · 2026Article
- A roadmap for medical large language models: a review of foundations, applications, and challenges.Military Medical Research · 2026Review
- Benchmarking large language models and clinicians using locally generated primary healthcare vignettes in Kenya.BMJ digital health & AI · 2026Article
Corrections and comments
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Authors and funding
38 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Large language models (LLMs) hold promise in clinical decision support but face major challenges in safety evaluation and effectiveness validation. We developed the Clinical Safety-Effectiveness Dual-Track Benchmark (CSEDB), a multidimensional framework built on clinical expert consensus, encompassing 30 metrics covering critical areas like critical illness recognition, guideline adherence, and medication safety, with weighted consequence measures. Thirty-two specialist physicians developed and revised 2069 open-ended Q&A items aligned with these criteria, spanning 26 clinical departments to simulate real-world scenarios. Benchmark testing of six LLMs revealed moderate overall performance (average total score 57.2%, safety 54.7%, effectiveness 62.3%), with a significant 13.3% performance drop in high-risk scenarios (p < 0.0001). Domain-specific medical LLMs showed consistent performance advantages over general-purpose models, with relatively higher top scores in safety (0.912) and effectiveness (0.861). The findings of this study not only provide a standardized metric for evaluating the clinical application of medical LLMs, facilitating comparative analyses, risk exposure identification, and improvement directions across different scenarios, but also hold the potential to promote safer and more effective deployment of large language models in healthcare environments.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.