What we ask
What makes an AI tool worthy of trust, and how do accuracy, transparency, oversight, fairness, and organizational readiness shape willingness to use it?
Emerging technology
Responsible AI implementation in healthcare
Studies of what healthcare professionals and communities need from AI systems before those tools can be responsibly integrated into care.
Project overview
Understanding the conditions that shape experience and confidence.
Responsible healthcare AI depends on more than technical performance. People must be able to understand how a tool supports decisions, who remains accountable, and whether the organization using it has the capacity to protect patients and professional judgment. This project examines responsibility as a property of both the technology and the system around it.
How we study it
Qualitative studies and evidence synthesis examine the expectations of health professionals and communities, with attention to implementation conditions rather than technology in isolation.
Where it leads
Findings support implementation approaches that preserve clinician judgment, make accountability visible, and give the people affected by AI a meaningful role in how it is introduced.
Why this matters
Research designed to inform practice.
Apparent resistance to AI may reflect inadequate training, weak infrastructure, unclear governance, or uncertainty about responsibility. By identifying these conditions, the work helps organizations distinguish skepticism that should be addressed from safeguards that should never be bypassed.
Practical impact
How the work can be used.
We connect research findings to concrete changes in practice, organizational decision-making, and the design of responsive systems.
Implementation readiness
Help organizations assess whether their training, infrastructure, workflows, and technical support are sufficient for responsible AI use.
Governance guidance
Clarify expectations for human oversight, accountability, transparency, privacy, and the protection of professional judgment.
Participatory design
Create stronger pathways for clinicians, patients, and communities to influence how AI tools are selected, introduced, evaluated, and improved.