ArticleNature medicine2025
Toward expert-level medical question answering with large language models.
Article in Nature medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 603 papers, 3 of them syntheses 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
603 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Fine-Tuning, Retrieval-Augmented Generation, and Hybrid Adaptation of Language Models for Clinical Decision-Making in Health Care: Systematic Review.Journal of medical Internet research · 2026Pooled it
- Application of Large Language Models in Chronic Disease Care: Mixed Methods Systematic Review and Thematic Synthesis.Journal of medical Internet research · 2026Pooled it
- Natural language generation in healthcare: A review of methods and applications.Journal of biomedical informatics · 2026Pooled it
- Consumer Understanding of Skin Concerns With an AI-Powered Informational Tool.JAMA dermatology · 2026Trial
- A cognitive layer architecture to support large-language model performance in psychotherapy interactions.Nature medicine · 2026Trial
- Article
- Fine-grained evaluation of a domain-specific Q&A dataset to support trustworthy medical language models.Health information science and systems · 2026Article
- The Rise of Small Language Models in Healthcare: A Comprehensive Survey.Computer science review · 2026Article
- How much do language and context affect the accuracy of AI health advice?BMJ health & care informatics · 2026Article
- ChatGPT Versus DeepSeek in Resectability Categorization of Chronic Rhinosinusitis: A Comparison Based on EPOS 2020.World journal of otorhinolaryngology - head and neck surgery · 2026Article
- Comparison of Two AI Chatbots for Diagnosis and Providing Treatment Suggestions in Retinopathy of Prematurity: Retrospective Study.Journal of medical Internet research · 2026Article
- Extraction of Pain Severity and Functional Interference From Clinical Narratives Using Domain-Informed Large Language Models: Protocol for a Development and Validation Study.JMIR research protocols · 2026Article
- Performance of large language models in the Chinese National Medical Licensing Examinations and Beyond: Scoping review.PLOS digital health · 2026Article
- The consensus-based CINEX guideline for reporting clinical information extraction studies.Journal of the American Medical Informatics Association : JAMIA · 2026Article
- Entity-centric evaluation of large language model responses for medical question-answering tasks.PLOS digital health · 2026Article
- Performance of large language models on the Japanese cardiovascular surgery board examination: a comparative analysis of eight contemporary AI models with educational implications.General thoracic and cardiovascular surgery · 2026Article
- Generative Language Models in Medical Education: From Advent to Entrustment.JMIR medical education · 2026Article
- Bilingual Performance of Large Language Models in Answering Consumer Health Questions in English and Chinese: Comparative Benchmark Study.Journal of medical Internet research · 2026Article
- RADAR: A Prototype for Grounding Large Language Model Consult Support in an ACR-Structured Appropriateness Knowledge Base with Citation Validation and Abstention.Journal of imaging informatics in medicine · 2026Article
- Development of a Framework for Evaluating Large Language Model Safety and Reliability: a Proof-of-Concept Evaluation.Journal of medical systems · 2026Article
543 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
35 authors.
Funding
Abstract
Large language models (LLMs) have shown promise in medical question answering, with Med-PaLM being the first to exceed a 'passing' score in United States Medical Licensing Examination style questions. However, challenges remain in long-form medical question answering and handling real-world workflows. Here, we present Med-PaLM 2, which bridges these gaps with a combination of base LLM improvements, medical domain fine-tuning and new strategies for improving reasoning and grounding through ensemble refinement and chain of retrieval. Med-PaLM 2 scores up to 86.5% on the MedQA dataset, improving upon Med-PaLM by over 19%, and demonstrates dramatic performance increases across MedMCQA, PubMedQA and MMLU clinical topics datasets. Our detailed human evaluations framework shows that physicians prefer Med-PaLM 2 answers to those from other physicians on eight of nine clinical axes. Med-PaLM 2 also demonstrates significant improvements over its predecessor across all evaluation metrics, particularly on new adversarial datasets designed to probe LLM limitations (P < 0.001). In a pilot study using real-world medical questions, specialists preferred Med-PaLM 2 answers to generalist physician answers 65% of the time. While specialist answers were still preferred overall, both specialists and generalists rated Med-PaLM 2 to be as safe as physician answers, demonstrating its growing potential in real-world medical applications.
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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.