Recalibrating Oversight: Post-Market Monitoring and Governance for AI-enabled Clinical Decision Support Software

Description: Clinical predictive models have flourished alongside AI advancements, supporting informed clinical decisions and optimizing resource allocations. Although highly accurate during testing, these models may not generalize across geographical settings such as different EU/EEA member states, or over time due to changes in patient populations (e.g., urban vs. rural), practice patterns (e.g., academic vs. community hospital), and data processes (e.g., variation in preprocessing or coding standards).
To ensure the long-term reliability and effectiveness of these devices, it is crucial that we establish systems for regular post-market monitoring and maintenance together with ethical provisions directly tied to patient safety, equity of access, and public trust. Continuous post-market monitoring is essential for detecting biases, performance degradation, or unintended harms, representing both a regulatory mandate and a moral imperative that extends beyond market authorization.
But post-deployment performance estimation becomes fundamentally unreliable once AI systems integrate into clinical causal pathways. Effective interventions triggered by model predictions can alter the incidence of predicted events, thereby changing the very outcomes the model aims to forecast. Such feedback loops may create an illusion of model decay or lead to apparent performance deterioration during subsequent clinical evaluations. Such confounding effects are extremely difficult to anticipate or study in premarket settings, where real-world interactions between predictions, clinician behavior, and patient outcomes are not yet observable. This creates a significant conundrum for both evaluation methodologies and regulatory oversight mechanisms.
This session will interpret post-market monitoring expectations for AI-enabled Clinical Decision Support Software through a principles‑based regulatory lens, aligned with broadly accepted expectations for lifecycle oversight. Grounded in ethical obligations to patient safety and equity, we will present practical strategies that support ongoing assurance of safety, clinical performance, and trust in real‑world use. We will debate intervention log analysis to assess clinical utility independent of traditional accuracy metrics, surrogate outcome markers as proxy measures when predicted events become rare following effective interventions, continuous monitoring, causal inference and human oversight.