ArticleNPJ digital medicine2025
Multiple large language models versus experienced physicians in diagnosing challenging cases with gastrointestinal symptoms.
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 16 papers.
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Who cites it
16 citing papers in PubMed.
- Article
- Large language models' performance in answering common patient questions about colonoscopy: an expert-based evaluation.BMC gastroenterology · 2026Observational
- Artificial intelligence in pituitary surgery: the path to clinical solutions.Endocrine-related cancer · 2026Review
- When silence is safer: a review and decision-theoretic framework for LLM abstention in healthcare.NPJ digital medicine · 2026Article
- Potential of large language models for rapid clinical information support: evidence from acute kidney injury knowledge testing.Scientific reports · 2026Article
- Optimizing prompting strategies improves large language model classification of pain- and fatigue-related functional impact in childhood cancer survivors.Communications medicine · 2026Article
- Benchmarking Large Language Models for Drug Combination Alerts: Achieving Expert-Level Reliability via Knowledge Grounding and Contextual Reasoning.Journal of medicinal chemistry · 2026Article
- Independent and collaborative performance of large language models and healthcare professionals in diagnosis and triage.NPJ digital medicine · 2026Article
- Large language models in Chinese anesthesiology residency examinations: a comparative analysis of performance, reliability and clinical reasoning.BMC medical education · 2026Article
- Artificial Authority: The Promise and Perils of LLM Judges in Healthcare.Bioengineering (Basel, Switzerland) · 2026Review
- Evaluating large language models' performance in answering common questions on drug-induced liver injury.JHEP reports : innovation in hepatology · 2025Article
- Evaluating the diagnostic accuracy of vision language models for neuroradiological image interpretation.NPJ digital medicine · 2025Article
- Grounded large language models for diagnostic prediction in real-world emergency department settings.JAMIA open · 2025Article
- Beyond the Growth: A Registry-Based Analysis of Global Imbalances in Artificial Intelligence Clinical Trials.Healthcare (Basel, Switzerland) · 2025Article
- Article
- Punching Above Its Weight: A Head-to-Head Comparison of Deepseek-R1 and OpenAI-o1 on Pancreatic Adenocarcinoma-Related Questions.International journal of medical sciences · 2025Article
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Authors and funding
34 authors.
Funding
Abstract
Faced with challenging cases, doctors are increasingly seeking diagnostic advice from large language models (LLMs). This study aims to compare the ability of LLMs and human physicians to diagnose challenging cases. An offline dataset of 67 challenging cases with primary gastrointestinal symptoms was used to solicit possible diagnoses from seven LLMs and 22 gastroenterologists. The diagnoses by Claude 3.5 Sonnet covered the highest proportion (95% confidence interval [CI]) of instructive diagnoses (76.1%, [70.6%-80.9%]), significantly surpassing all the gastroenterologists (p < 0.05 for all). Claude 3.5 Sonnet achieved a significantly higher coverage rate (95% CI) than that of the gastroenterologists using search engines or other traditional resource (76.1% [70.6%-80.9%] vs. 45.5% [40.7%-50.4%], p < 0.001). The study highlights that advanced LLMs may assist gastroenterologists with instructive, time-saving, and cost-effective diagnostic scopes in challenging cases.
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Registered trials
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