Navigating Healthcare AI Governance: the Comprehensive Algorithmic Oversight and Stewardship Framework for Risk and...

Area of Science:

• Healthcare innovation
• Artificial intelligence ethics
• Regulatory science

Background:

• Integrating artificial intelligence (AI) into healthcare presents significant innovation opportunities.
• However, the rapid adoption of AI has outpaced regulatory oversight, leading to vulnerabilities.
• Unregulated "shadow" AI systems pose risks including algorithmic bias, drift, and health disparities.

Purpose of the Study:

• To introduce a novel framework for the ethical and responsible implementation of AI in healthcare.
• To address the identified gaps in regulatory oversight for AI systems.
• To provide a scalable solution for managing AI risks and promoting equitable healthcare.

Main Methods:

• Development of the Comprehensive Algorithmic Oversight and Stewardship (CAOS) Framework.
• Integration of risk assessment methodologies.
• Incorporation of data protection principles and equity-focused approaches.

Main Results:

• The CAOS Framework offers a structured approach to govern AI in healthcare.
• It combines normative governance with practical system design for AI implementation.
• The framework facilitates ethical oversight, policy development, and operational management of AI systems.

Conclusions:

• The CAOS Framework provides a robust solution to bridge healthcare AI oversight gaps.
• It supports continued innovation while mitigating risks associated with AI.
• This framework promotes responsible AI stewardship in the healthcare sector.