Volume 5, Issue 4 (2025) – 7 articles
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Aim: This scoping review aimed to synthesize current evidence on the application of artificial intelligence (AI), including natural language processing (NLP) and large language models (LLMs), in post-polypectomy surveillance for colorectal cancer (CRC). Specific objectives were to assess technological advances, evaluate their impact on guideline adherence, and identify gaps for future research.
Methods: We conducted a scoping review following the Arksey and O’Malley framework and PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines. Searches across PubMed, EMBASE, Scopus, and Web of Science identified studies applying AI to CRC surveillance after polypectomy. Eligible studies investigated AI models for interval assignment or risk stratification using colonoscopy and pathology data.
Results: Of 950 screened articles, seven met the inclusion criteria. Five studies used NLP-based decision support tools, achieving concordance rates of 81.7%-99.9% with guideline-recommended surveillance intervals, consistently outperforming clinician recommendations. Two studies evaluated ChatGPT-4 in clinical decision making; fine-tuned models demonstrated an accuracy of up to 85.7%, surpassing that of physicians in retrospective and simulated scenarios. NLP systems demonstrated technical maturity and scalability, while LLMs offered flexible, user-friendly interfaces but were less reliable in complex clinical scenarios.
Conclusion: AI tools, particularly NLP-enhanced systems, demonstrate strong potential to standardize post-polypectomy surveillance and improve guideline adherence. LLMs are promising but remain under validation. Future research should assess clinical implementation, long-term outcomes, and integration within electronic health records.
view this paper Aim: This scoping review aimed to synthesize current evidence on the application of artificial intelligence (AI), including natural language processing (NLP) and large language models (LLMs), in post-polypectomy surveillance for colorectal cancer (CRC). Specific objectives were to assess technological advances, evaluate their impact on guideline adherence, and identify gaps for future research.
Methods: We conducted a scoping review following the Arksey and O’Malley framework and PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines. Searches across PubMed, EMBASE, Scopus, and Web of Science identified studies applying AI to CRC surveillance after polypectomy. Eligible studies investigated AI models for interval assignment or risk stratification using colonoscopy and pathology data.
Results: Of 950 screened articles, seven met the inclusion criteria. Five studies used NLP-based decision support tools, achieving concordance rates of 81.7%-99.9% with guideline-recommended surveillance intervals, consistently outperforming clinician recommendations. Two studies evaluated ChatGPT-4 in clinical decision making; fine-tuned models demonstrated an accuracy of up to 85.7%, surpassing that of physicians in retrospective and simulated scenarios. NLP systems demonstrated technical maturity and scalability, while LLMs offered flexible, user-friendly interfaces but were less reliable in complex clinical scenarios.
Conclusion: AI tools, particularly NLP-enhanced systems, demonstrate strong potential to standardize post-polypectomy surveillance and improve guideline adherence. LLMs are promising but remain under validation. Future research should assess clinical implementation, long-term outcomes, and integration within electronic health records.
