Routine use of AI in colonoscopies linked to decreased skill in adenoma detection by clinicians without AI assistance.
Key Details
- 1Observational study involved 1,443 non-AI assisted colonoscopies across four centers in Poland.
- 2After routine AI adoption, experienced endoscopists' adenoma detection rate fell from 28.4% to 22.4% (a 20% relative decrease) in procedures without AI.
- 3AI-assisted procedures maintained a 25.3% detection rate during the same period.
- 4Authors caution the findings may indicate a risk of skill erosion (deskilling) for health professionals with extensive AI use.
- 5Study calls for further research into long-term consequences of AI in clinical workflows and strategies to preserve clinicians’ skills.
Why It Matters
This is the first real-world study suggesting that continuous use of imaging AI tools could degrade clinicians’ core diagnostic abilities, raising important questions about safety, training, and oversight as AI adoption in endoscopy—and wider radiology—accelerates.
Source
EurekAlert
Related News
•EurekAlert
Deep Learning AI Outperforms Clinic Prognostics for Colorectal Cancer Recurrence
A new deep learning model using histopathology images identifies recurrence risk in stage II colorectal cancer more effectively than standard clinical predictors.
•EurekAlert
AI Reveals Key Health System Levers for Cancer Outcomes Globally
AI-based analysis identifies the most impactful policy and resource factors for improving cancer survival across 185 countries.
•EurekAlert
Dual-Branch Graph Attention Network Predicts ECT Success in Teen Depression
Researchers developed a dual-branch graph attention network that uses structural and functional MRI data to accurately predict individual responses to electroconvulsive therapy in adolescents with major depressive disorder.