How smart can colonoscopy become?

The clinical evidence for AI-assisted colonoscopy is strong. Multiple recent studies show that AI can significantly increase both adenoma detection rate (ADR) and polyp detection rate (PDR). A large multicenter European study published in 2025 reported an ADR of 59.1 percent with AI support compared to 46.6 percent with standard colonoscopy, while PDR increased from 56.2 percent to 69.8 percent.

These improvements matter clinically: higher ADR is directly associated with a lower risk of interval colorectal cancer. At the same time, studies also show that AI does not replace good endoscopic technique – it acts as an assistive layer that supports consistency and attention.

The market for AI in endoscopy is fragmented. Many solutions currently focus on colonoscopy, particularly on real-time detection (CADe), but they differ significantly in sensitivity, false-positive rates and functional scope.

For clinics, performance alone is not enough. Key questions increasingly include:
What happens beyond detection? How well does AI integrate into existing workflows? And how much additional effort does it create – or eliminate? In practice, clinics should look at three dimensions: detection performance, workflow integration and operational impact.

AI only becomes attractive if it makes operational sense. In colonoscopy, automated documentation, structured reporting, and direct integration into hospital information systems can significantly reduce administrative workload. Early real-world data suggest that automated reporting can reduce documentation time by up to 80 percent, saving five to ten minutes per examination and often more than one hour of physician time per day.

Improved detection performance may also affect quality indicators and reimbursement, making economic impact a relevant consideration for hospital management and procurement teams.

MAIA Labs serves as an example of a broader shift in AI development – from standalone detection tools to workflow-oriented systems. They exemplify a new generation of AI solutions that move beyond detection alone. ColoMAIA combines real-time AI with end-to-end workflow automation – from patient identification and voice-based documentation to structured reporting and direct integration into hospital information systems (HIS).

The case study illustrates how AI can make colonoscopy not only more precise, but also more efficient and consistent. It highlights a broader trend: the real value of AI often lies in workflow and reporting, not in detection alerts alone.