Methodology for Safe and Secure AI in Diabetes Management

Area of Science:

• Medical Informatics
• Artificial Intelligence
• Diabetes Technology

Background:

• Artificial intelligence (AI) shows promise for enhancing diabetes management through improved monitoring and personalized therapies.
• Clinical integration of AI in diabetes care presents significant challenges concerning patient data safety, security, and regulatory compliance.
• Potential direct impacts on patient health necessitate careful consideration of AI system design and implementation.

Purpose of the Study:

• To provide guidance for developers and researchers on addressing safety, security, and compliance challenges in AI systems for diabetes management.
• To highlight the critical role of explainable AI (xAI) in ensuring security, compliance, and user trust for AI-driven diabetes solutions.
• To examine both technical and regulatory aspects crucial for developing trustworthy and effective explainable AI applications in diabetes care.

Main Methods:

• Analyzing the AI system lifecycle to integrate xAI frameworks, security measures, and risk mitigation strategies.
• Examining technical methodologies for constructing xAI systems throughout the AI development process.
• Reviewing regulatory frameworks, including Governance, Risk, and Compliance (GRC) standards from bodies like the FDA, for AI-enabled healthcare applications.

Main Results:

• Understanding the AI system lifecycle is key to building robust xAI frameworks that address security and mitigate risks.
• Regulatory analysis identifies essential GRC standards for ensuring the safety, efficacy, and ethical integrity of AI in diabetes care.
• Integrating technical and regulatory insights provides a pathway for developing trustworthy AI solutions.

Conclusions:

• Explainable AI (xAI) is foundational for secure, compliant, and trustworthy AI in diabetes management, enhancing clinical decision-making.
• Addressing both technical development and regulatory requirements is essential for safe and effective AI-enabled diabetes care.
• Actionable insights are provided to foster patient engagement and improve clinical outcomes through reliable AI solutions.