AI Polyp Detection in Colonoscopy Needs More Rigorous Testing Before Clinical Adoption

Colorectal cancer remains one of the leading causes of cancer death worldwide, yet it is highly preventable when precancerous polyps are identified and removed during routine colonoscopy. The accuracy of polyp detection therefore carries enormous clinical stakes, making it a natural target for artificial intelligence-assisted tools.

This commentary, published in Gut, critically evaluates the emerging use of large language models for detecting colorectal polyps in endoscopic images. The author, based at Ben-Gurion University of the Negev, argues that despite rapid advances in LLM capabilities, the evidence base supporting their deployment in this specific diagnostic task is not yet mature enough for clinical recommendation.

The piece does not present new experimental data but instead offers a critical appraisal of the current literature and methodological landscape. The author highlights that existing studies may lack the rigorous validation, diverse patient populations, and prospective design necessary to establish reliability. The commentary echoes a growing concern among clinicians that enthusiasm for AI tools can outpace the evidence needed to confirm their safety and efficacy.

From a practical standpoint, the implications are significant. Gastroenterologists and endoscopists considering AI-assisted colonoscopy platforms should be aware that LLM-based systems, in particular, require more robust clinical trials before they can be trusted as reliable diagnostic aids. Premature adoption risks both missed polyps and false positives.

The broader takeaway is a call for the research community to prioritize well-designed, prospective studies that test LLM performance against established benchmarks and in real-world clinical settings. Until such evidence exists, these tools should be considered experimental rather than standard of care.