ArticleEndoscopy international open2025
Comparing ChatGPT3.5 and Bard recommendations for colonoscopy intervals: Bridging the gap in healthcare settings.
Article in Endoscopy international open, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 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
- Responsible use of large language models in gastroenterology and hepatology.Therapeutic advances in gastroenterology · 2026Review
- Between hype and hard evidence: Are large language models ready for implementation in surveillance colonoscopy?Endoscopy international open · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background and study aims: Colorectal cancer is a leading cause of cancer-related deaths, with screening and surveillance colonoscopy playing a crucial role in early detection. This study examined the efficacy of two freely available large language models (LLMs), GPT3.5 and Bard, in recommending colonoscopy intervals in diverse healthcare settings. Patients and methods: A cross-sectional study was conducted using data from routine colonoscopies at a large safety-net and a private tertiary hospital. GPT3.5 and Bard were tasked with recommending screening intervals based on colonoscopy reports and pathology data and their accuracy and inter-rater reliability were compared to a guideline-directed endoscopist panel. Results: Of 549 colonoscopies analyzed (N = 268 at safety-net and N = 281 private hospital), GPT3.5 showed better concordance with guideline recommendations (GPT3.5: 60.4% vs. Bard: 50.0%, Conclusions: This study highlights the limitations of freely available LLMs in assisting colonoscopy screening recommendations. Although the potential of freely available LLMs to offer uniformity is significant, the low accuracy, as noted, excludes their use as the sole agent in providing recommendations.
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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.