ArticleNature medicine2024
Adapted large language models can outperform medical experts in clinical text summarization.
Article in Nature medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 384 papers, 4 of them syntheses that pooled it.
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Who cites it
384 citing papers in PubMed, 4 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
- Natural language generation in healthcare: A review of methods and applications.Journal of biomedical informatics · 2026Pooled it
- Testing and evaluation of generative large language models in electronic health record applications: a systematic review.Journal of the American Medical Informatics Association : JAMIA · 2026Pooled it
- Performance and improvement strategies for adapting generative large language models for electronic health record applications: A systematic review.International journal of medical informatics · 2026Pooled it
- A large language model for complex cardiology care.Nature medicine · 2026Trial
- Reliability of LLMs as medical assistants for the general public: a randomized preregistered study.Nature medicine · 2026Trial
- From patient notes to prognostication: The revolutionary potential of event-based foundation models in orthopaedics.Knee surgery, sports traumatology, arthroscopy : official journal of the ESSKA · 2026Article
- A Controlled Single-Speaker Evaluation of Ambient Voice Technologies' Speech-to-Text Function Using Narrated Surgical Case-Reports.Journal of medical systems · 2026Article
- From Innovation to Impact: The CREATE Framework as a Blueprint for Large Language Model Adoption in Opioid Treatment Programs.Journal of medical Internet research · 2026Article
- The consensus-based CINEX guideline for reporting clinical information extraction studies.Journal of the American Medical Informatics Association : JAMIA · 2026Article
- Augmenting structured diagnoses through effective use of pre-trained large language models on clinical notes.JAMIA open · 2026Article
- Optimizing generative artificial intelligence for clinical summarization: a blinded comparison study of automated versus human care planning synopses.JAMIA open · 2026Article
- Artificial Intelligence-Generated Electronic Medical Record Summarization in Breast Surgical Oncology.Annals of surgical oncology · 2026Article
- A Large Language Model for Extracting Post-marketing Adverse Drug Events from Clinical Notes in the Electronic Health Record.Drug safety · 2026Article
- Sentence-Level Provenance for AI Medical Record Summarization in a Click-to-Inspect Interface: Formative Usability Evaluation.JMIR human factors · 2026Article
- Less Is More: Radiologists Prefer Brief Narratives in LLM-Generated Clinical History Summaries.Journal of imaging informatics in medicine · 2026Article
- Patient-Centered Summarization Framework for AI Clinical Summarization: Mixed Methods Study.Journal of medical Internet research · 2026Article
- Generative large language models in medicine: a scoping review of recent methodological advances.npj health systems · 2026Review
- Applying Large Language Models in Perioperative Medicine With an Equity-Informed Perspective.Anesthesia and analgesia · 2026Article
- Artificial intelligence in clinical genetics: Current practice and attitudes among the clinical genetics workforce.Genetics in medicine : official journal of the American College of Medical Genetics · 2026Article
324 more citing papers are in PubMed but not listed here.
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Authors and funding
19 authors.
Funding
Abstract
Analyzing vast textual data and summarizing key information from electronic health records imposes a substantial burden on how clinicians allocate their time. Although large language models (LLMs) have shown promise in natural language processing (NLP) tasks, their effectiveness on a diverse range of clinical summarization tasks remains unproven. Here we applied adaptation methods to eight LLMs, spanning four distinct clinical summarization tasks: radiology reports, patient questions, progress notes and doctor-patient dialogue. Quantitative assessments with syntactic, semantic and conceptual NLP metrics reveal trade-offs between models and adaptation methods. A clinical reader study with 10 physicians evaluated summary completeness, correctness and conciseness; in most cases, summaries from our best-adapted LLMs were deemed either equivalent (45%) or superior (36%) compared with summaries from medical experts. The ensuing safety analysis highlights challenges faced by both LLMs and medical experts, as we connect errors to potential medical harm and categorize types of fabricated information. Our research provides evidence of LLMs outperforming medical experts in clinical text summarization across multiple tasks. This suggests that integrating LLMs into clinical workflows could alleviate documentation burden, allowing clinicians to focus more on patient care.
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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.