JAMA network open · 2026
Consumer Perspectives on Trust in and Benefits of Artificial Intelligence in Health Care.
СтатьяНазвание и аннотация: Перевод в очереди
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Duong Tuan, Plage Stefanie, Woods Leanna, Dillon Miriam, Olsen Quita, Kirwa Titus, Brown Anna, Wang Wenyong, Sullivan Clair
Аннотация
IMPORTANCE: Artificial intelligence (AI) has the potential to improve patient-centered care, yet several AI health care projects have failed due to public backlash, underscoring the importance of social license (informal public acceptance of AI in health care, grounded in trust and expectation of public benefit). How consumers conceptualize social license remains largely unknown.
OBJECTIVES: To explore social license for AI in health care among consumers and to identify strategies for achieving it.
DESIGN, SETTING, AND PARTICIPANTS: A participatory qualitative study using the comfort board method was conducted across workshops in Queensland, Australia, in September 2025, applying abductive reflexive thematic analysis. Participants were recruited using a mix of convenience and purposive sampling. Eligibility criteria included being aged 18 years or older, residing in Queensland, self-reported receiving care in a Queensland health care facility within the past 24 months, and able to communicate in English.
MAIN OUTCOMES AND MEASURES: Concept of social license for AI in health care and strategies for achieving it.
RESULTS: Thirty-four participants (21 [62%] female) were included, with diverse ages (16 [53%] aged 31-60 years), countries of birth, disability statuses, and AI familiarity. Key themes of social license for AI included relational engagement, structural support, and performance reliability. Performance reliability was critical in short-term care, whereas relational engagement and structural support were more salient in long-term care. According to participants, achieving social license would require AI systems to function as supportive clinical tools, governed by strict stakeholder-driven standards and developed through codesign that aligns them with care needs, contexts, and patient-clinician interactions.
CONCLUSIONS AND RELEVANCE: This qualitative study of consumer perspectives on AI in health care found that social license for AI is a conditional and dynamic construct not a fixed state; structural, performance, and relational factors intersected to shape social license. The findings provide evidence-based recommendations for stakeholders designing and implementing AI in clinical settings, highlighting the need for AI tools designed to support both consumers and clinicians in delivering care that is personalized, empathetic, and responsive to patient needs.
OBJECTIVES: To explore social license for AI in health care among consumers and to identify strategies for achieving it.
DESIGN, SETTING, AND PARTICIPANTS: A participatory qualitative study using the comfort board method was conducted across workshops in Queensland, Australia, in September 2025, applying abductive reflexive thematic analysis. Participants were recruited using a mix of convenience and purposive sampling. Eligibility criteria included being aged 18 years or older, residing in Queensland, self-reported receiving care in a Queensland health care facility within the past 24 months, and able to communicate in English.
MAIN OUTCOMES AND MEASURES: Concept of social license for AI in health care and strategies for achieving it.
RESULTS: Thirty-four participants (21 [62%] female) were included, with diverse ages (16 [53%] aged 31-60 years), countries of birth, disability statuses, and AI familiarity. Key themes of social license for AI included relational engagement, structural support, and performance reliability. Performance reliability was critical in short-term care, whereas relational engagement and structural support were more salient in long-term care. According to participants, achieving social license would require AI systems to function as supportive clinical tools, governed by strict stakeholder-driven standards and developed through codesign that aligns them with care needs, contexts, and patient-clinician interactions.
CONCLUSIONS AND RELEVANCE: This qualitative study of consumer perspectives on AI in health care found that social license for AI is a conditional and dynamic construct not a fixed state; structural, performance, and relational factors intersected to shape social license. The findings provide evidence-based recommendations for stakeholders designing and implementing AI in clinical settings, highlighting the need for AI tools designed to support both consumers and clinicians in delivering care that is personalized, empathetic, and responsive to patient needs.