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Building trust: public priorities for health care AI labeling
Morgan L Sielaff1, Jodyn Platt2, Sean Tan2
1Department of Learning Health Sciences, University of Michigan Medical School, 2800 Plymouth Rd, North Campus Research Complex, Bldg 14, Room G016, Ann Arbor, MI 48109.
Objectives:
Labeling and the use of model cards have been promoted as ways to increase transparency for multiple end users. This study aimed to identify key content for a health artificial intelligence (AI) tool label based on public perspectives and expectations.
Study Design:
We used a mixed-methods study design, combining public deliberation and pre-/post surveys to inform participants about AI in health care and gather input on key information for a health AI tool label.
Methods:
In 2024, we conducted 5 virtual community deliberations across Michigan, engaging 159 participants in facilitated small-group discussions that were qualitatively coded. Participants completed a 20-minute survey before and after the deliberation to assess changes in knowledge, attitudes, and trust regarding AI in health care.
Results:
Participants prioritized information regarding privacy and security, health equity, and safety and effectiveness of AI tools for inclusion on a health AI tool label. An AI label is, therefore, a familiar and transparent mechanism to build trust and address patients' desire for notification.
Conclusions:
The findings highlight ethical gaps in using AI in health care settings and the value of publicly informed, patient-centered solutions. There is strong demand for clear, accessible information on how AI tools are used and their risks and benefits. A patient-informed label may address these ethical challenges and improve transparency, trust, and patient-centered communication as AI reshapes health care.
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