Clinical Scorecard: Governance of Artificial Intelligence in Healthcare Systems: A Comprehensive Review of Existing Frameworks and a Proposed Integrative Model
At a Glance
Category
Detail
Condition
Artificial Intelligence Governance in Healthcare Systems
Key Mechanisms
Operational structures, processes, and mechanisms to translate ethical principles into practice.
Target Population
Healthcare organizations and stakeholders involved in AI integration.
Care Setting
Health systems and digital health environments.
Key Highlights
AI integration raises expectations and challenges in health systems.
Governance frameworks are essential for operationalizing ethical AI principles.
Existing frameworks often address issues separately rather than interdependently.
A systematic review identifies recurrent shortcomings in AI governance frameworks.
Proposed model aims to guide AI-related policy, practice, and research.
Guideline-Based Recommendations
Diagnosis
Identify relevant actors and allocate decision-making authority.
Management
Establish auditable requirements for data governance and model validation.
Monitoring & Follow-up
Implement post-deployment monitoring and incident reporting.
Risks
Address ethical, sociopolitical, economic, and clinical challenges.
Patient & Prescribing Data
Not specified; relevant to all stakeholders in healthcare systems.
Focus on responsible and effective integration of AI technologies.
Clinical Best Practices
Engage stakeholders in transparent public communication.
Ensure accountability mechanisms are in place.
Evaluate clinical effectiveness, safety, and equity impacts.
