It's 2026, and healthcare AI is still seeking what researchers call its "social license" - the informal, tacit public acceptance that goes beyond legal approval. A new study published Aug. 4 in JAMA Network Open identifies three factors that determine whether patients trust AI enough to let it shape their care.
Led by academics at the University of Queensland in Australia, the research team conducted workshops with 34 patients who had received care in Queensland facilities within the past two years. The participants, aged 31 to 60, shared how their experiences shaped their willingness to accept AI in clinical settings.
Their conclusion: "Together, trust and perceptions of benefits are central determinants of the social license for AI in healthcare."
Relational engagement
Patients' trust in their clinicians directly influenced their trust in AI, the researchers found. Most participants wanted to be told when AI was used and when their data was being used.
"However, recognizing clinicians' time constraints, some participants reported they would not mind if someone else introduced this information, allowing clinicians to focus more on their care," the authors wrote.
Structural support
Participants expected AI governance that was clear, dedicated, and established. They didn't view oversight as a constraint but as a condition for acceptance.
"Rather than positioning governance as a means to constrain AI use, participants saw governance as a necessary condition to enable and enhance its social license," the authors wrote. This view was strongest in long-term care scenarios. In short-term emergencies - periods participants described as highly stressful and frightening - structural factors took a back seat to immediate care.
Performance reliability
Patients wanted AI systems with a proven track record of timely, accurate guidance. While many expressed high trust in AI accuracy, others raised concerns based on personal experiences where AI had failed.
The study authors suggest their work is the first to show how healthcare consumers' perceptions either advance or hold back AI's social license. They frame the findings as practical guidance for policymakers.
"In the settings of increasing need for refining and coordinating governance and regulatory frameworks at local, state and national levels, our findings provide actionable guidance for the sustainable integration of AI in healthcare while upholding principles of patient-centered care," they wrote.
Why this matters for healthcare leaders
For executives planning AI investments, the study offers a clear warning: clinical accuracy alone won't secure patient buy-in. Trust in AI is built on the strength of patient-clinician relationships, transparent communication about data use, and governance structures patients can see and understand.
Healthcare organizations that treat AI governance as a public-facing priority - not just an internal compliance exercise - are more likely to earn the social license that makes adoption sustainable. That means training clinical staff to discuss AI use with patients and establishing clear policies for when and how AI informs decisions, particularly in long-term care settings where patients expect more oversight. For practical guidance on building these capabilities, AI for Healthcare resources can help clinical teams understand what patients need to know. Leaders looking to align AI strategy with patient expectations may also benefit from AI for Executives & Strategy approaches that treat governance as a foundation for care delivery.
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