ArticleNPJ digital medicine2025
A framework to assess clinical safety and hallucination rates of LLMs for medical text summarisation.
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 176 papers, 2 of them syntheses that pooled it.
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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
176 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Knowledge-Practice Performance Gap in Clinical Large Language Models: Systematic Review of 39 Benchmarks.Journal of medical Internet research · 2025Pooled it
- Clinical applications of large language models in knee osteoarthritis: a systematic review.Frontiers in medicine · 2025Pooled it
- Generative AI-enabled clinical decision support system in primary care: a pragmatic, cluster-randomized trial.Nature medicine · 2026Trial
- A large language model for clinical outcome adjudication from telephone follow-up interviews: a secondary analysis of a multicenter randomized clinical trial.Nature communications · 2025Trial
- A Controlled Single-Speaker Evaluation of Ambient Voice Technologies' Speech-to-Text Function Using Narrated Surgical Case-Reports.Journal of medical systems · 2026Article
- OsteoCHAT: real-world patient evaluation and benchmarking of a guideline-grounded osteoporosis chatbot.Osteoporosis international : a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA · 2026Article
- Entity-centric evaluation of large language model responses for medical question-answering tasks.PLOS digital health · 2026Article
- Effect of large language model on diagnostic accuracy and clinical completeness among nephrology fellows managing transplant infection.International urology and nephrology · 2026Article
- Performance of large language models in ambiguous treatment scenarios of early breast cancer.Breast (Edinburgh, Scotland) · 2026Article
- Article
- Large language models for tympanostomy patient education: readability and guideline adherence.European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery · 2026Article
- Performance of Cloud-Hosted Large Vision-Language Models on the Japanese National Examination for Clinical Laboratory Technicians: Comparative Benchmarking Study.JMIR medical education · 2026Article
- Sentence-Level Provenance for AI Medical Record Summarization in a Click-to-Inspect Interface: Formative Usability Evaluation.JMIR human factors · 2026Article
- Perspectives on the Limits and Clinical Alignment of Medical AI from Population Statistics to Individual Care.Bioengineering (Basel, Switzerland) · 2026Article
- Multimodal Large Language Models for Dental Chart Image Interpretation: Cross-Sectional Benchmarking Study With Students and Clinicians.JMIR medical education · 2026Article
- Toward continuous quality observability in medicine.npj health systems · 2026Review
- Hierarchical multi-label structuring of Japanese SOAP clinical notes with large language models.Scientific reports · 2026Article
- Comments on "Evaluating the Efficacy of Large Language Models in Generating Medical Documentation: A Comparative Study of ChatGPT-4, ChatGPT-4o, and Claude".Aesthetic plastic surgery · 2026Article
- Assessment of AI-Generated Patient Education Materials for Bladder Training and Pelvic Floor Muscle Therapy: Comparison with an International Society Leaflet.International urogynecology journal · 2026Article
- Transforming hemodialysis care: a tripartite collaboration model among medical staff, AI agents, and robots.Clinical and experimental nephrology · 2026Review
116 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Integrating large language models (LLMs) into healthcare can enhance workflow efficiency and patient care by automating tasks such as summarising consultations. However, the fidelity between LLM outputs and ground truth information is vital to prevent miscommunication that could lead to compromise in patient safety. We propose a framework comprising (1) an error taxonomy for classifying LLM outputs, (2) an experimental structure for iterative comparisons in our LLM document generation pipeline, (3) a clinical safety framework to evaluate the harms of errors, and (4) a graphical user interface, CREOLA, to facilitate these processes. Our clinical error metrics were derived from 18 experimental configurations involving LLMs for clinical note generation, consisting of 12,999 clinician-annotated sentences. We observed a 1.47% hallucination rate and a 3.45% omission rate. By refining prompts and workflows, we successfully reduced major errors below previously reported human note-taking rates, highlighting the framework's potential for safer clinical documentation.
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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.