Responsible AI in Canadian Healthcare: Evaluation Frameworks, Challenges, and Policy Directions.

Area of Science:

• Healthcare technology
• Artificial intelligence ethics
• Public health policy

Background:

• Artificial intelligence (AI) is revolutionizing healthcare delivery, impacting areas like medical imaging and predictive analytics.
• Despite AI's potential, significant concerns regarding fairness, transparency, accountability, and equity persist.
• Canada's public healthcare system and robust AI sector are actively developing responsible AI (RAI) frameworks.

Purpose of the Study:

• To conduct a scoping review of responsible AI (RAI) frameworks in Canadian healthcare.
• To identify existing strengths and persistent gaps in the governance and implementation of RAI in Canada.

Main Methods:

• A comprehensive scoping review methodology was employed.
• Literature search and synthesis focused on RAI in the Canadian healthcare context.

Main Results:

• Canada demonstrates a strong emphasis on interdisciplinary coalition-building for AI development.
• Significant focus is placed on workforce preparedness and AI training initiatives.
• Key gaps identified include the absence of robust post-market surveillance mechanisms for AI tools.
• Insufficient attention is paid to explicit equity safeguards and sustainable funding models for RAI implementation.

Conclusions:

• While Canada excels in fostering collaboration and preparing its workforce for AI in healthcare, critical governance and implementation gaps remain.
• The current frameworks lack essential components for ongoing monitoring, ensuring equity, and securing long-term financial viability.
• Addressing these deficiencies is crucial for the responsible and equitable integration of AI into Canada's healthcare system.