Keeping Clinical AI Healthy: How We Prevent Algorithm Burnout in Medicine
AI in healthcare isn’t a “set it and forget it” solution. Clinical algorithms degrade over time—new data patterns, shifting demographics, or evolving protocols can silently erode accuracy.In this episode of AI in Medicine, we unpack a critical new review:How performance drift happens in diagnostic and triage modelsThe detection methods that spot issues earlyBest practices for retraining, validation, and auditingWhy “algorithm health” is essential for clinician trust and patient safetyWhether you build AI tools or deploy them in hospitals, this is a must-hear foundation for sustaining impact in the long run.