Beyond initial approval, what continuous oversight is needed for healthcare AI?

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Continuous Oversight is Essential for Healthcare AI Safety and Reliability

Beyond initial regulatory approval, healthcare AI systems necessitate ongoing monitoring to ensure their continued accuracy, reliability, and fairness. Unlike conventional medical equipment, AI performance can be dynamic, influenced by factors such as software updates, evolving datasets, changes in patient demographics, and shifts in clinical practices. This necessitates a proactive approach to oversight to maintain patient trust and safety.
Medical Physicists' Evolving Role in AI Governance

Medical physicists are increasingly vital in the continuous oversight of healthcare AI, extending their expertise from traditional machinery to sophisticated algorithms. Their traditional responsibilities in quality assurance and performance evaluation for equipment like CT scanners and MRI machines provide a strong foundation for managing AI systems. This expertise is crucial for questioning and verifying the performance of AI tools within specific local contexts.
Addressing Localised Performance and Bias in AI

A critical aspect of continuous oversight involves ensuring AI systems perform adequately across diverse patient populations and local healthcare environments. An AI tool trained on data from one region, such as Europe or North America, may not yield the same results when applied to patients in Malaysia, due to differences in demographics, disease patterns, and clinical workflows. Medical physicists are essential in asking whether AI systems have been adequately tested locally and if their performance remains consistent across different patient groups.
Monitoring AI Performance Post-Implementation and Updates

The dynamic nature of AI means that their performance can change, particularly after software updates or modifications to training datasets. Continuous oversight requires diligent monitoring of these systems to detect any degradation in accuracy or the emergence of biases. Questions regarding performance changes after software updates and the adequacy of data protection measures are central to this ongoing evaluation process, ensuring that AI remains a safe and effective tool in clinical practice.

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Beyond initial approval, what continuous oversight is needed for healthcare AI?

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