ArticleBMC medical informatics and decision making2024
Assessing the research landscape and clinical utility of large language models: a scoping review.
Article in BMC medical informatics and decision making, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 111 papers, 9 of them syntheses that pooled it.
What it found
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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.
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Who cites it
111 citing papers in PubMed, 9 syntheses or guidelines pooled it.
- Accuracy of Large Language Models in Answering Dental Examination Questions: A Systematic Review and Meta-Analysis.International dental journal · 2026Pooled it
- Artificial intelligence adoption in healthcare: a systematic review of implementation challenges and health services implications.BMC health services research · 2026Pooled it
- Large language models in healthcare: applications, evaluation frameworks, and governance pathways - a scoping review and multidimensional framework.Frontiers in digital health · 2026Pooled it
- AI in Medical Questionnaires: Scoping ReviewJournal of medical Internet research · 2025Pooled it
- Pooled it
- Accuracy of Large Language Models When Answering Clinical Research Questions: Systematic Review and Network Meta-Analysis.Journal of medical Internet research · 2025Pooled it
- A systematic review of large language model (LLM) evaluations in clinical medicine.BMC medical informatics and decision making · 2025Pooled it
- STEM exam performance: Open- versus closed-book methods in the large language model era.The clinical teacher · 2025Pooled it
- Applications and Concerns of ChatGPT and Other Conversational Large Language Models in Health Care: Systematic Review.Journal of medical Internet research · 2024Pooled it
- Optimizing generative artificial intelligence for clinical summarization: a blinded comparison study of automated versus human care planning synopses.JAMIA open · 2026Article
- Alignment between AI clinical decision tools and multidisciplinary tumor board decisions in prostate cancer.International urology and nephrology · 2026Article
- Large language models' performance in answering common patient questions about colonoscopy: an expert-based evaluation.BMC gastroenterology · 2026Observational
- AI-Enabled Interpretation Guidance for Hemostasis Testing with TEGDiagnostics (Basel, Switzerland) · 2026Article
- Methods of Evaluating Large Language Model-Based Health Care Applications Used by Nonprofessionals: Protocol for a Scoping Review.JMIR research protocols · 2026Article
- A Self-Controlled Benchmark of Retrieval-Augmented Generation for Large Language Models on Clinical Guideline Questions.Diagnostics (Basel, Switzerland) · 2026Article
- Automated Review of Patient Records: Privacy-Preserving Large Language Models for Identifying Incident Nonarteritic Anterior Ischemic Optic Neuropathy at Scale.Ophthalmology science · 2026Article
- Evaluating Large Language Models for AI-Assisted Decision Support in Legal Capacity Assessment: A Comparative Study Using Interdisciplinary Medical Board Recommendations as the Expert Medical Reference Standard.Healthcare (Basel, Switzerland) · 2026Article
- A S.C.O.R.E. framework for evaluating open-ended responses from large language models in healthcare.Cell reports. Medicine · 2026Article
- Value of the DeepSeek-R1 large language model in extracting structured data from magnetic resonance imaging reports of rectal cancer and assisting in tumor staging.Quantitative imaging in medicine and surgery · 2026Article
- From Promising Capabilities to Pervasive Bias: Assessing Large Language Models for Emergency Department Triage.Journal of healthcare informatics research · 2026Article
51 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
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
importanceLarge language models (LLMs) like OpenAI's ChatGPT are powerful generative systems that rapidly synthesize natural language responses. Research on LLMs has revealed their potential and pitfalls, especially in clinical settings. However, the evolving landscape of LLM research in medicine has left several gaps regarding their evaluation, application, and evidence base.
objectiveThis scoping review aims to (1) summarize current research evidence on the accuracy and efficacy of LLMs in medical applications, (2) discuss the ethical, legal, logistical, and socioeconomic implications of LLM use in clinical settings, (3) explore barriers and facilitators to LLM implementation in healthcare, (4) propose a standardized evaluation framework for assessing LLMs' clinical utility, and (5) identify evidence gaps and propose future research directions for LLMs in clinical applications. EVIDENCE REVIEW: We screened 4,036 records from MEDLINE, EMBASE, CINAHL, medRxiv, bioRxiv, and arXiv from January 2023 (inception of the search) to June 26, 2023 for English-language papers and analyzed findings from 55 worldwide studies. Quality of evidence was reported based on the Oxford Centre for Evidence-based Medicine recommendations.
findingsOur results demonstrate that LLMs show promise in compiling patient notes, assisting patients in navigating the healthcare system, and to some extent, supporting clinical decision-making when combined with human oversight. However, their utilization is limited by biases in training data that may harm patients, the generation of inaccurate but convincing information, and ethical, legal, socioeconomic, and privacy concerns. We also identified a lack of standardized methods for evaluating LLMs' effectiveness and feasibility. CONCLUSIONS AND RELEVANCE: This review thus highlights potential future directions and questions to address these limitations and to further explore LLMs' potential in enhancing healthcare delivery.
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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.