ArticleDigital health
Multidimensional assessment of ChatGPT in colorectal cancer postoperative consultations: Analysing response variations across critical clinical domains.
Article in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 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.
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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
1 citing paper 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
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Colorectal cancer (CRC) represents a substantial global burden, particularly in China. Patients have limited awareness of the importance of postoperative follow-up examinations and insufficient knowledge about review, which ultimately leads to poor long-term prognosis. The advent of artificial intelligence tools such as chat-generating pre-trained transformer (ChatGPT) in the healthcare sector is poised to transform patient management strategies. Objective: The objective of this study was to evaluate the effectiveness of ChatGPT in responding to patients' inquiries regarding postoperative follow-up of CRC. The overarching objective is to enhance patients' awareness of, and compliance with, postoperative follow-up examinations. Methods: A set of 10 questions concerning postoperative review of CRC was posed to ChatGPT4.5. The responses to these inquiries were evaluated in three domains (accuracy, completeness, and comprehensibility) by five anorectal specialists in Zhejiang Province, in conjunction with 100 inpatients in three domains (completeness, comprehensibility, and trustability). Result: The accuracy scale (scoring from 1 to 6) received an average score of 4.6 ± 0.7. The completeness (scoring from 1 to 3) and comprehensibility (scoring from 1 to 3) scales received average scores of 2.2 ± 0.5 and 2.5 ± 0.5, respectively. Cronbach's α analyses indicated good reliability for the accuracy and completeness scales (α = 0.85) and excellent reliability for the comprehensibility scale (α = 0.93). However, they also suggested possible redundancy issues. Patient feedback was positive, with 98% to 100% rating all questions as completeness, comprehensibility, and trustability. Conclusion: ChatGPT has been demonstrated to possess the capacity to formulate satisfactory responses to inquiries concerning CRC postoperative review. Furthermore, it has the potential to enhance patient awareness and knowledge, which may consequently lead to an improvement in long-term outcomes.
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Registered trials
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