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 models
The detection methods that spot issues early
Best practices for retraining, validation, and auditing
Why “algorithm health” is essential for clinician trust and patient safety
Whether you build AI tools or deploy them in hospitals, this is a must-hear foundation for sustaining impact in the long run.