ArticleInternational journal of surgery (London, England)2026
Guideline adherence in surgical decisions for T1 colorectal cancer after endoscopic resection: large language models vs clinicians.
Article in International journal of surgery (London, England), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.
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Who cites it
2 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
- Artificial intelligence in surgical decision-making across the perioperative continuum: a scoping review.Frontiers in digital health · 2026Review
Corrections and comments
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Authors and funding
6 authors.
Funding
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Abstract
backgroundPatients with T1 colorectal cancer (CRC) often show poor adherence to guideline-recommended treatment strategies after endoscopic resection. To address this challenge and improve clinical decision-making, this study aims to compare the accuracy of surgical management recommendations between large language models (LLMs) and clinicians.
methodsThis retrospective study enrolled 202 patients with T1 CRC who underwent endoscopic resection at three hospitals. We compared the decision-making accuracy of two representative LLMs (ChatGPT-4o and DeepSeek) with that of 29 clinicians in determining whether additional surgery was required after endoscopic resection of T1 CRC. To optimize the inputs for the LLMs, we applied a prompt-engineering strategy that combined role prompting, in-context learning, and few-shot learning.
resultsIn clinical practice, we found that the guideline adherence rate after endoscopic resection for T1 CRC was below 80%. We analyzed the pathology reports of 200 patients with T1 CRC, and the results showed that both ChatGPT-4o and DeepSeek significantly outperformed clinical physicians. Subgroup analyses demonstrated that LLMs outperformed doctors regardless of years of experience or professional background. Additionally, the use of Chinese or English input had no significant impact on the performance of the LLMs.
conclusionThis study highlights the potential of LLMs to improve guideline adherence in the post-endoscopic resection management of T1 CRC.
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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.