Assessment of patient and physician sentiment on artificial intelligence use in US healthcare

Abstract

Background Medical applications of artificial intelligence (AI) range from diagnostic support and electronic health record optimization to personalized treatment and administrative automation. Despite these advances, AI integration into healthcare requires the acceptance and trust of clinicians and patients. Understanding their perspectives is critical to guiding effective and ethical AI adoption in medicine. Methods We conducted a nationwide, anonymous, online survey of self-identified physicians and patients in the United States using the Clinician and Patient Experience Registry (CaPER) platform. The survey employed Random Domain Intercept Technology (RDIT) and Random Device Engagement (RDE) to collect nationally-representative online responses while minimizing known survey biases. Respondents were stratified into physicians ( n = 382) or patients ( n = 760), and completed a series of questions assessing demographics, comfort with AI-supported decision-making, trust in AI vs. human clinicians, and perceived impact of AI on the physician-patient relationship. Data were analyzed descriptively and comparatively, including specialty-specific sub-analyses among physicians. Results A total of 1,142 complete responses were analyzed. Both physicians and patients reported generally positive attitudes toward AI-supported medical decision-making, with the majority expressing comfort or neutrality. Approximately one-third of both groups favored a collaborative model integrating both human and AI input. Specialty-specific analysis revealed higher comfort with AI among procedure-based disciplines, while diagnostic-oriented specialties expressed more reservations. Respondents were generally evenly divided regarding the anticipated impact of AI on the physician-patient relationship, with many predicting a strengthening effect. Conclusions This large-scale online survey highlights a generally favorable outlook toward AI integration among both physicians and patients, with notable variation by medical specialty for physicians. The findings underscore the importance of tailoring AI implementation strategies to specific clinical contexts and maintaining a focus on human-AI collaboration.

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