Abandonment of Prescribed Buprenorphine for Opioid Use Disorder, 2020-2024.

Artificial intelligence (AI), computational systems that can perform tasks that are commonly performed by human intelligence, has emerged as a transformative technology, demonstrating potential to enhance clinical practice and patient outcomes. Despite the increasing need for AI-enabled care, many nations do not yet have a fully unified national framework guiding the use of AI in health care. International legal frameworks, such as the European Union Artificial Intelligence Act and the US National Policy Artificial Intelligence Framework, provide additional contexts for evolving international standards. Beyond legislation, AI development and deployment must adhere to ethical standards. The World Health Organization provides key principles for ethical AI use in health care, including autonomy, well-being, transparency, accountability, inclusiveness, equity, and sustainability. However, successful implementation of AI in health care requires more than compliance with laws and ethical approval. Several health data projects have met both legislative and ethical standards and yet failed due to public backlash. This highlights the importance of social license as a critical factor for the success of AI in health care. Social license refers to a dynamic, informal, and tacit form of public acceptance granted for organizations undertaking AI-related activities in health care contexts, such as developing, deploying, or governing these technologies. Importantly, social license is not equivalent to legal compliance or regulatory approval; rather, it is grounded in public trust and expectation that these activities will align and promote public benefits. While perceptions about benefits can potentially affect trust, trust in turn moderates how these perceptions influence public responses to AI. Together, trust and perceptions of benefits are central determinants of the social license for AI in health care. Social license for AI health care includes both the sharing of health data for AI development and the use of AI technologies in clinical practice. Previous literature indicates increasing consumer awareness of health data sharing in the context of AI, with generally positive attitudes toward sharing personal health data for AI purposes. Emerging evidence also suggests broad support for AI as an assistive tool in clinical decision-making, with many consumers expressing trust in AI-enabled diagnosis and treatment. However, these studies focus mainly on consumer perspectives toward specific AI-related activities, particularly data sharing, leaving a notable gap in understanding how consumers conceptualize social license for AI in health care. Addressing this gap requires examining the foundations of social license from health consumer perspectives, with particular attention to trust and perceived benefit. Our study focused on the overarching research question: How can a consumer social license for AI in health care be achieved? Specifically, our study aimed to explore social license for AI in health care among consumers and to identify and propose strategies for achieving social license for AI in health care.