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

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The Comprehensive Algorithmic Oversight and Stewardship (CAOS) Framework tackles risks from unregulated artificial intelligence (AI) in healthcare. It ensures responsible AI innovation by combining risk assessment, data protection, and equity.

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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.