Accelerating the Responsible & Equitable Adoption of AI Through Education
As artificial intelligence (AI) becomes increasingly embedded within healthcare, there is a growing need to prepare healthcare professionals and organizations to use these technologies safely, effectively, and equitably. This work focuses on building the knowledge, skills, and practical tools needed to support responsible AI adoption across healthcare settings.
This work began with an earlier phase, Accelerating the Adoption of Artificial Intelligence in Healthcare. During this time, we worked with clinicians, patients, and partners to develop and test practical approaches to AI use across healthcare settings.
Building on that foundation, the current phase, Spread & Scale of AI for All, focuses on bringing what we learned into real-world settings. This phase looks at how these approaches can be integrated into professional education, everyday workflows, and organizational systems to support broader adoption across healthcare.
How This Work Is Approached
To support this work, we use a Mindset-Skillset-Toolset (MST) framework, a layered approach that recognizes effective AI integration in healthcare depends not only on available tools, but on the attitudes and capabilities of the people using them.
Mindset
building awareness, trust, and confidence in AI
Skillset
developing the ability to use and assess AI in practice
Toolset
providing guidance and systems that support responsible use
Together, this framework helps ensure AI can be applied in ways that are practical, consistent, and sustainable across healthcare.
Vision for Impact
This work aims to support a shift toward routine and embedded use of AI across healthcare systems. By strengthening education, developing shared tools, and supporting integration within organizational systems, it helps move AI from early exploration into everyday practice. As AI continues to evolve, this work helps healthcare systems become better equipped to adapt while maintaining safe, equitable, and patient-centered care.
What We’ve Been Working On
Patient Charter of Rights for AI in Healthcare
Co-developed with patients and caregivers to define expectations for how AI should be used.
CARE-AI Framework (co-developed with Equity in Health Systems Lab)
A practical tool to support more equitable and accountable AI use.
AI education programs
Including mental health and generative AI training to support real-world application
Overview
This workstream focuses on establishing clear expectations for how artificial intelligence should be used in healthcare by centering patient and caregiver perspectives.
Core Work
A national engagement process involving surveys, interviews, and co-design sessions led to the development of the Patient Charter of Rights for Accountable Use of AI in Canadian Healthcare. The Charter outlines key expectations such as transparency, accountability, and appropriate use, and is intended to help guide how AI is used across healthcare settings.
Impact
It provides a shared reference point for how AI should be used, supporting greater consistency, building trust, and aligning expectations across patients, providers, and organizations.
Overview
This workstream focuses on advancing equitable use of AI by addressing bias and supporting more inclusive approaches to implementation.
Core Work
In collaboration with the Equity in Health Systems Lab (EQHS), this work resulted in the CARE-AI Framework and Equity Toolkit. This framework offers structured guidance, supported by scenario based resources, to help teams identify and address bias in real world settings.
Impact
It strengthens how equity is considered in AI-related decisions, supporting more consistent and inclusive practices across clinical, educational, and operational contexts.
Overview
This workstream looks at how AI can be applied in mental health and addiction care, where careful consideration of context, risk, and sensitivity is essential.
Core Work
Key activities included a four-week education course, a mentorship program, and a national symposium
focused on AI in mental health. Together these activities created opportunities to explore practical use, work through real-world challenges, and consider the ethical and clinical implications specific to this area of care.
Impact
It supports more informed and context-sensitive use of AI in mental health settings, helping build confidence and judgement in applying these tools.
Overview
This workstream supports understanding and use of generative AI tools within healthcare environments.
Core Work
A generative AI education program was developed across UHN and delivered through webinars, in-person sessions, and hands-on learning to support practical use. This includes guidance on safe and appropriate use, such as privacy and data considerations, along with practical application of tools like Microsoft Copilot. A resource hub at UHN was also created to support ongoing learning by providing easy access to materials and resources.
Impact
It supports more consistent and confident use of generative AI across roles, helping staff apply these tools in ways that are practical, safe, and aligned with organizational expectations.
Overview
This workstream focuses on strengthening how AI education is coordinated across healthcare and academic partners.
Core Work
The work has centered on building partnerships to share approaches, align efforts, and better understand how AI education can be delivered across different contexts. This included identifying common needs, exploring gaps in current learning opportunities, and supporting the development of approaches that can be applied across multiple settings.
Impact
It helps bring greater coordination to AI education in healthcare, making it easier for organizations to align their efforts and build workforce readiness in a more consistent and scalable way.
Overview
This workstream focuses on building a more coordinated approach to defining AI literacy and AI learning across healthcare and academic settings.
Core Work
At the center of this work is the development of a structured education series and collaborative learning environment that brings together clinicians, educators, and system leaders. Sessions are designed around real-world use of AI in areas such as diagnosis, treatment planning, and administrative workflows, while also addressing questions related to safety, privacy, governance, and equity. The format emphasizes practical discussion and shared learning, creating space for participants to explore how AI is being used in their own settings and what needs to be considered before adopting it more broadly.
Impact
It helps build a more shared and practical understanding of AI across healthcare, supporting organizations and professionals in making more informed decisions about how these tools are introduced and used.
Funding
This work is led and implemented by University Health Network and is made possible through funding from the Future Skills Centre, which supports UHN to advance, embed, and scale this work across the healthcare system.
Fostering Equity and Inclusion in Healthcare by Accelerating Responsible Use of Artificial Intelligence is funded by the Government of Canada under the Future Skills program. Le projet Promouvoir l’équité et l’inclusion dans les soins de santé en accélérant l’utilisation responsable de l’intelligence artificielle est financé par le gouvernement du Canada dans le cadre du programme Compétences futures
Thank you for your interest in our research. If you would like to learn more about our work and publications, or have any questions or comments, we welcome the opportunity to connect with you.
