ReviewHealth care science2026
A Survey on Medical Competence Evaluation Benchmarks for Large Language Models.
Review in Health care science, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Large Language Models in Colorectal Cancer Care and Clinical Decision Support: Systematic Review.Journal of medical Internet research · 2026Pooled it
- Evaluation of large language model-generated information in diabetes health patient education: a scoping review.Frontiers in public health · 2026Article
- AI-enabled language technologies for language-mediated learning and clinical communication in international undergraduate dental education: a scoping review.Frontiers in medicine · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Large language models (LLMs) show considerable potential to revolutionize healthcare through their performance across diverse clinical applications. Given the inherent constraints of LLMs and the critical nature of medical practice, a rigorous and systematic evaluation of their medical competence is imperative. This study presents a comprehensive review of the established methodologies and benchmarks for evaluating the medical competence of LLMs, encompassing a thorough analysis of current assessment practices across medical knowledge, clinical practice competence, and ethical-safety considerations. By integrating clinician competency assessment frameworks into LLMs evaluation, we propose a structured tri-dimensional framework that systematically organizes existing evaluation approaches according to medical theoretical knowledge, clinical practice ability, and ethical-safety considerations. Furthermore, this research provides critical insights into future developmental trajectories while establishing foundational frameworks and standardization protocols for the integration of LLMs into medical practice.
Indexed as
Identifiers
What OpenQuestion holds
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.