Advanced AI Driven Post Marketing Surveillance for SaMD: Risk Monitoring, Clinical Feedback Integration, and Regulatory Compliance

Abstract

Although AI based Software as a Medical Device (SaMD) has truly transformed diagnostics and personalized health, flexible post marketing surveillance (PMS) methodologies fail to characterize AI SaMDs. As a result, there are gaps in the evaluation of risk, the integration of clinical feedback, and the requirements for regulatory compliance. A better PMS framework with deep learning models for anomaly analysis TCNs, clinical feedback assessment HANs, and synthetic data generation cGANs addresses the limitations of risk monitoring, advanced feedback appraisal, and automatic compliance checks. This development proposes a new solution to the ever evolving challenges of AI SaMDs. Experiment results show drastic improvements: a 75% Risk Impact, an 85% Clinical Follow up Effectiveness, a 95% Compliance Score, Performance Deviation reduced to 4.20%, and Data Integration Efficiency improved up to 90%. Our framework outperforms the existing ones in risk detection, stability, and integration efficiency compared to existing methods in proactive risk mitigation and robust real world monitoring. This work paves the way towards AI SaMD monitoring as it deals with the inadequacies posed by traditional PMS in making AI health related solutions safer, more reliable, and regulatory compliant, with future opportunities for multi modal data extension, federated learning for privacy preservation, and explainable AI (XAI) for greater interpretability and trust.