Beyond Validation: Operationalising Post-Deployment Surveillance of AI Medical Devices in Clinical Practice.

Area of Science:

• Medical Informatics
• Clinical Engineering
• Artificial Intelligence in Healthcare

Background:

• Artificial intelligence (AI) medical devices are increasingly common in clinical settings.
• Current methods for post-deployment monitoring of these AI systems are not well-established.
• Lack of defined monitoring strategies poses challenges for safe and accountable AI use.

Purpose of the Study:

• To present a structured, decision-oriented framework for monitoring AI medical devices post-deployment.
• To provide practical guidance for healthcare institutions managing AI technologies.
• To enhance the safety and accountability of AI integration into clinical workflows.

Main Methods:

• Development of a decision-oriented monitoring model based on institutional deployment experience.
• Framing AI surveillance as a series of interdependent decisions.
• Integrating performance assessment with governance-linked corrective actions.

Main Results:

• The proposed model offers a structured approach to AI medical device surveillance.
• It facilitates effective performance assessment and governance.
• Enables timely and appropriate corrective actions for AI systems.

Conclusions:

• A structured, decision-oriented approach is essential for effective post-deployment monitoring of AI medical devices.
• This framework supports safer and more accountable integration of AI into clinical practice.
• The model provides a practical strategy for healthcare institutions to manage AI technologies effectively.