Physicians’ Attitudes Toward Artificial Intelligence in Medicine: Mixed Methods Survey and Interview Study

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Abstract
Background: Artificial intelligence (AI) has the potential to transform clinical practice and diagnostics. Amid workforce shortages, AI-based applications assist in decision-making, patient monitoring, and administrative tasks. However, despite enthusiasm, integration into clinical practice remains limited because of concerns about usability, ethical implications, and physicians’ acceptance. Understanding physicians’ attitudes and engaging them in AI development may foster acceptance and adoption. Objective: This study aimed to comprehensively assess physicians’ attitudes toward AI in medicine. Methods: We conducted a mixed methods study combining a web-based survey and qualitative interviews. The survey explored physicians’ perspectives on the advantages and disadvantages of AI, its role in decision-making, and impact on physician-patient communication. Attitudes were measured using a 5-point Likert scale, covering cognitive and affective dimensions. An exploratory factor analysis (EFA) identified underlying attitudinal factors, while the Mann-Whitney U and the Kruskal-Wallis tests examined differences in attitudes based on physicians’ age, discipline, AI familiarity, and other variables. Overall, 13 physicians, independent of the survey sample, participated in semistructured interviews, which were analyzed using inductive coding and thematic analysis. Results: The survey yielded 498 responses. EFA revealed two factors: (1) AI enthusiasm and acceptance (Cronbach α=0.83) and (2) AI skepticism and apprehension (Cronbach α=0.77). Physicians reported high AI enthusiasm (median 4, IQR 3.57-4.29) and lower skepticism (median 3.62, IQR 3.20-4.20; reverse coded, with higher scores indicating reduced skepticism). Greater AI familiarity, use in daily life or professionally, and involvement in research were strongly associated with greater enthusiasm and lower skepticism. Physicians involved in AI-related research reported significantly higher enthusiasm (mean rank: AI research=111.52; no AI research=54.32; P<.001) and lower skepticism (mean rank: AI research=108.27; no AI research=70.45; P=.01). Those using AI professionally or intending to do so similarly expressed high enthusiasm (mean rank: professional use=253.88; no use=196.17; P=.001) and lower skepticism (mean rank: plan to use=275.93; no use=218.86; P=.001). Greater familiarity with AI tools was also strongly associated with higher enthusiasm (mean rank: very familiar=323.55; not familiar=169.86; P<.001) and lower skepticism (mean rank: very familiar=296.90; not familiar=186.23; P=.008). Chief physicians (mean rank 277.32) were significantly less skeptical than residents (mean rank 210.60; P=.01); however, age and discipline did not influence attitudes. The qualitative analysis identified six themes shaping physicians’ attitudes: (1) status quo, (2) AI dependency and negligence, (3) role changes and needs, (4) AI transparency and decision-making, (5) the physician-patient relationship, and (6) a framework for responsible AI integration. These findings led to several key propositions considered critical for AI adoption. Conclusions: AI in medicine is viewed positively, with attitudes shaped more by experience and engagement than by demographic factors. While concerns persist, they diminish with increased familiarity and professional use. These findings highlight the need for targeted education, hands-on training, and standardized implementation strategies to enhance AI engagement and facilitate adoption.

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