ArticleJournal of surgical oncology2024
Accuracy and consistency of publicly available Large Language Models as clinical decision support tools for the management of colon cancer.
Article in Journal of surgical oncology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
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Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Large Language Models in Colorectal Cancer Care and Clinical Decision Support: Systematic Review.Journal of medical Internet research · 2026Pooled it
- The effects of multitype prompt engineering for large language models in hypertension treatment decisions.NPJ digital medicine · 2026Article
- Comparative Performance of Large Language Models on European Gastroenterology Board-Style Questions: Analysis of Reasoning Versus Non-Reasoning Architectures.Journal of clinical medicine · 2026Article
- The ACPGBI AI taskforce report: A mixed-methods roadmap for AI in colorectal surgery.Colorectal disease : the official journal of the Association of Coloproctology of Great Britain and Ireland · 2025Article
- Assessing the accuracy of the GPT-4 model in multidisciplinary tumor board decision prediction.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2025Article
- The Role of Artificial Intelligence (ChatGPT-4o) in Supporting Tumor Board Decisions.Journal of clinical medicine · 2025Article
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- Artificial intelligence in healthcare education: evaluating the accuracy of ChatGPT, Copilot, and Google Gemini in cardiovascular pharmacology.Frontiers in medicine · 2025Article
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Authors and funding
9 authors.
Funding
Abstract
backgroundLarge Language Models (LLM; e.g., ChatGPT) may be used to assist clinicians and form the basis of future clinical decision support (CDS) for colon cancer. The objectives of this study were to (1) evaluate the response accuracy of two LLM-powered interfaces in identifying guideline-based care in simulated clinical scenarios and (2) define response variation between and within LLMs.
methodsClinical scenarios with "next steps in management" queries were developed based on National Comprehensive Cancer Network guidelines. Prompts were entered into OpenAI ChatGPT and Microsoft Copilot in independent sessions, yielding four responses per scenario. Responses were compared to clinician-developed responses and assessed for accuracy, consistency, and verbosity.
resultsAcross 108 responses to 27 prompts, both platforms yielded completely correct responses to 36% of scenarios (n = 39). For ChatGPT, 39% (n = 21) were missing information and 24% (n = 14) contained inaccurate/misleading information. Copilot performed similarly, with 37% (n = 20) having missing information and 28% (n = 15) containing inaccurate/misleading information (p = 0.96). Clinician responses were significantly shorter (34 ± 15.5 words) than both ChatGPT (251 ± 86 words) and Copilot (271 ± 67 words; both p < 0.01).
conclusionsPublicly available LLM applications often provide verbose responses with vague or inaccurate information regarding colon cancer management. Significant optimization is required before use in formal CDS.
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