AI Polyp Detection Tools Face Deskilling and Trust Challenges

AI detection gains come with clinical trade-offs

Artificial intelligence tools designed to spot polyps during colonoscopy are delivering mixed results in real-world gastroenterology practice, according to recent clinical commentaries published in leading medical journals. While computer-aided detection (CADe) systems can identify more adenomas during procedures, they may simultaneously erode physician skills and create workflow friction that undermines their effectiveness.

Research examining AI-assisted colonoscopy in high-performing screening programs found that CADe systems increased overall adenoma detection counts but failed to improve detection of advanced adenomas—the clinically significant lesions that matter most for cancer prevention. More concerning, observational data suggests endoscopists who routinely use CADe show reduced adenoma detection rates when performing procedures without AI assistance, according to analysis by Ignasi Puig, MD, PhD, and Maria Pellisé, MD, PhD, published in the United European Gastroenterology Journal.

Why it matters

These findings challenge the assumption that adding AI to clinical workflows automatically improves outcomes. For healthcare organizations investing in AI-assisted endoscopy systems, the research suggests that technology adoption requires careful attention to training, workflow design, and performance monitoring—not just procurement and deployment. The deskilling phenomenon raises questions about long-term clinical competency in an AI-augmented environment.

False positives undermine physician trust

A key barrier to effective AI integration is the high rate of false alerts. Studies report CADe systems generate approximately 26-27 false activations per colonoscopy, creating noise and cognitive load that erodes clinician trust. According to commentary in Gastroenterology by Alan Barkun, MD, and colleagues, some endoscopists respond by deactivating the AI tools entirely.

The research, first reported by Medscape, reveals that physicians adopt different delegation strategies based on their career stage and trust calibration. Early-career clinicians tend to respond to most AI alerts, mid-career physicians use CADe as a safety net while learning to filter false positives, and late-career endoscopists often disengage due to perceived limited benefit relative to the added cognitive burden.

Validation and threshold selection remain critical gaps

Clinical evaluation practices for gastroenterology AI tools lack consistency and interpretability, according to analysis by Federico Cabitza, PhD, and colleagues published in Digestive and Liver Disease. The researchers argue that AI performance should be assessed at prespecified, clinically justified decision thresholds rather than optimized for statistical metrics that may not reflect real-world priorities.

One practical framework they propose is the "K-heuristic"—asking how many false positives clinicians will accept to avoid one false negative. In low-prevalence settings, even systems with 90% sensitivity and 80% specificity can generate hundreds of false alarms for every true case detected. A CADe system applied to 1,000 patients with 0.5% cancer prevalence produced approximately 199 false positives alongside 0.5 false negatives in one study example.

The authors emphasize that independent validation on external datasets, transparency about data similarity, and adherence to regulatory requirements are essential to assess generalizability and enable safe deployment.

Designing for human-AI collaboration

The European Society of Gastrointestinal Endoscopy has issued only a weak recommendation for CADe adoption, citing limited evidence of impact on colorectal cancer incidence and mortality alongside increased surveillance burden. The guidance reflects growing recognition that AI tools are context-sensitive rather than universal solutions.

Effective implementation requires understanding how different clinicians delegate detection work to AI, actively monitoring false-positive rates and cognitive load, and providing targeted training that matches workflow integration to physician expertise levels. As one commentary notes, "Understanding and designing [AI tools] for specific delegation patterns may constitute the key to consistent effectiveness."

These findings were first reported by Medscape in their AI Watch coverage of gastroenterology practice.