New AI tool automates colonoscopy quality assessment at scale

Colonoscopies are one of the most effective ways to prevent colorectal cancer, which is surging among young adults in the U.S. Yet, the quality of each procedure can vary substantially from one physician to another. As a result, medical societies recommend routinely reviewing every colonoscopist - something that's challenging and time-consuming for practices to implement.

Dr. Rajesh Keswani, study lead author, associate professor of medicine, division of gastroenterology and hepatology at Northwestern University Feinberg School of MedicineOur AI software provides a scalable approach to measuring colonoscopy quality and, ultimately, providing feedback to clinicians to improve care quality."

Colonoscopies let physicians examine patients' large intestines, or colons, via an inserted tube that's equipped with a camera and with tools to remove precancerous polyps. A high-quality colonoscopy generally means a physician inspects the entire colon, spends enough time looking for abnormalities and uses recommended techniques to remove suspicious polyps.

Previous studies have shown that measuring colonoscopy performance improves the quality of colonoscopy overall and, ultimately, reduces colorectal cancer mortality. By automating that quality-monitoring process, the new tool could make colonoscopy feedback more feasible across entire hospitals or healthcare systems, said Keswani, who also is a Northwestern Medicine physician.

Findings

The AI tool analyzed nearly 18,600 colonoscopies performed by 55 physicians over 11 months at Northwestern. For the study, the AI software reviewed colonoscopy recordings and identified key moments, such as when the tube reached the beginning of the colon, when it was withdrawn and when polyps were removed.

The tool's measurement of withdrawal time - a key metric that tracks time spent examining the colon as the scope is being withdrawn - closely matched times recorded by nurses. That showed the technology is highly accurate, the authors said.

The AI also tracked quality indicators that "can't be feasibly measured by humans at scale," according to Keswani. These included the number of polyps removed during a procedure and how often physicians used cold snare polypectomy, a guideline-recommended technique for removing small polyps.

AI concerns

"One possibility is that physicians rely too much on AI, which leads to deskilling," he said. "Alternatively, AI can teach us about blind spots and make us better clinicians."

Keswani added that his research team is currently studying the role and impact of AI in teaching colonoscopy to trainees.

Source:

Northwestern University

Journal reference:

Keswani, R. N., et al. (2026). Artificial Intelligence Automated Assessment of Colonoscopy Quality Metrics. American Journal of Gastroenterology. DOI: 10.14309/ajg.0000000000004170. https://www.ovid.com/jnls/ajg/fulltext/10.14309/ajg.0000000000004170~artificial-intelligence-automated-assessment-of-colonoscopy