ACCEPTANCE of artificial intelligence (AI) in healthcare by patients has been influenced by a range of psychological, social, and system-level factors, according to a systematic review published in JMIR. The findings highlighted that trust, transparency, and usability were among the most consistent determinants of whether patients were willing to engage with AI-enabled healthcare tools.
AI healthcare acceptance shaped by trust and usability factors
A systematic review identified that patient acceptance of AI in healthcare was strongly linked to perceived trust in technology, concerns over data privacy, and the clarity of communication from healthcare providers. Patients were more likely to accept AI when they understood how it supported clinical decision-making and when it was integrated into familiar care pathways.
Alongside trust, ease of use and perceived benefit played a central role in shaping attitudes. Where AI tools were seen as improving efficiency, accuracy, or access to care, acceptance was higher. However, fear of reduced human interaction and uncertainty around accountability were commonly reported barriers.
Barriers to AI healthcare acceptance in patients
Key barriers to AI healthcare acceptance included concerns about data security, lack of understanding of how algorithms functioned, and fear of misdiagnosis or over-reliance on automated systems. The review also highlighted that limited digital literacy and poor communication about AI use further reduced willingness to engage with these technologies.
Cultural and demographic differences were also identified, with acceptance varying depending on age, prior healthcare experiences, and familiarity with digital tools.
Facilitators of AI healthcare acceptance and future use
Facilitators of patient acceptance included clear explanation of AI’s role in care, clinician endorsement, and transparent governance frameworks. The review suggested that when AI was presented as a supportive tool rather than a replacement for clinicians, patients demonstrated greater willingness to engage.
Importantly, the authors noted that building trust through education and shared decision-making was essential to improving acceptance levels across healthcare systems.
Implications for AI integration in healthcare
The authors noted that improving patient acceptance of AI healthcare tools will require targeted communication strategies, stronger transparency around data use, and greater emphasis on human and AI collaboration. They also emphasised that future implementation efforts should prioritise patient-centred design to ensure AI technologies are aligned with user expectations and real-world clinical workflows.
Reference
Shi H et al. Barriers and facilitators to patient acceptance of artificial intelligence in health care: systematic review. J Med Internet Res. 2026;28:e80581. DOI:10.2196/80581.
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