A comprehensive multi-center study from Poland raises alarms about how brief exposure to AI-assisted colonoscopy may diminish physicians’ independent detection skills. The findings challenge the notion that AI automatically enhances care and prompt Thai health leaders to scrutinize how rapid AI integration could affect clinicians in screening programs.
In Thailand, colorectal cancer remains a major public health concern, accounting for a meaningful share of new cancer diagnoses. High-quality detection during colonoscopies is crucial for early treatment and better survival, making it essential to understand how AI tools influence physician performance, especially when AI is not actively guiding the procedure.
The Polish investigation analyzed colonoscopies performed at four medical centers, comparing adenoma detection rates before and after AI system implementation. Using patient-adjusted analyses, the study examined 1,443 colonoscopies conducted without AI assistance to isolate any indirect effects of prior AI exposure on subsequent exams. The results showed a six-percentage-point drop in detection rates after AI exposure, from 28.4% to 22.4%. Researchers emphasized that this decline persisted even after accounting for age, gender, and medical history, highlighting potential deskilling when clinicians rely on automated prompts during earlier procedures.
Experts caution that three months may be insufficient to declare lasting skill impairment. They call for longer-term follow-up to determine whether the effect endures or dissolves with continued practice and training. The study’s lead investigator noted that physicians appeared to defer to AI cues, waiting for green-highlight indicators rather than performing independent visual assessments.
AI in endoscopy involves real-time analysis that flags suspicious areas and presents visual cues to the endoscopist. While prior work showed improved detection during active AI use, this study focuses on physicians’ performance when AI is unavailable. The term deskilling captures the concern that automation may erode essential clinical vigilance.
Broader international observations echo these questions. In radiology and other imaging fields, experts report that non-expert clinicians sometimes perform worse when AI support is anticipated but not present. The rapid pace of AI adoption across health systems amplifies these concerns, underscoring the need for safeguards that preserve human diagnostic skills.
Thailand’s push toward AI-powered medical imaging and workflow optimization makes understanding deskilling risk especially timely. Colorectal cancer remains a significant health burden, and many Thai communities still face barriers to screening access. As regional hospitals shoulder screening responsibilities across diverse populations, preserving clinician competence—both with and without AI assistance—becomes critical to sustaining high-quality care.
Thai cultural expectations emphasize trusted physician expertise and transparent decision-making. Families often rely on doctors’ judgments when choosing screening options, so physician confidence in diagnostic skills is vital for public trust. If AI support is perceived as replacing human judgment, trust could be endangered; conversely, if doctors actively manage AI tools, patient confidence may grow.
This moment highlights gaps in AI education within Thai medical training. Many senior clinicians began practice before AI tools existed, raising questions about how best to equip the workforce for responsible technology integration. Strengthening AI literacy and ensuring robust simulation training can help clinicians maintain independent diagnostic abilities alongside AI benefits.
Policy and practice guidance emerges as a priority. System leaders should design AI programs that pair deployment with continuous performance monitoring and explicit training on when to rely on AI versus human judgment. Regulators may require post-deployment evaluation protocols to track changes in clinician behavior and diagnostic accuracy after AI adoption.
Hospitals should implement routine measurement of detection performance with and without AI activation and develop fallback protocols for AI failures. Medical education institutions should expand curricula to include AI fundamentals, algorithm limitations, and strategies to maintain core diagnostic skills through simulated practice without AI support.
Beyond clinical guidelines, Thai stakeholders should pursue long-term research to verify whether observed effects persist and how training interventions might mitigate potential deskilling. Local studies that replicate the Polish approach can tailor evidence to Thai clinical practice and inform policy decisions with domestic data.
In practice, AI design should support clinicians without encouraging passive reliance. Interfaces that require clinician validation before acting on AI prompts can help preserve critical thinking and pattern recognition. Public communication should clearly explain AI’s role in procedures, aiming to maintain patient trust and informed consent.
Actionable steps for Thai healthcare authorities include developing comprehensive AI training programs, implementing performance monitoring after AI rollouts, and refining clinical protocols to balance AI-assisted efficiency with independent diagnostic skills. By grounding AI adoption in evidence and robust education, Thailand can harness AI’s benefits while safeguarding patient safety and physician excellence.