Background Artificial Intelligence (AI) has increasingly been integrated into clinical practice, yet the adoption and perception of AI among medical professionals remain poorly understood, particularly in the Italian healthcare context.

Objective To investigate Italian physicians’ knowledge, attitudes, and clinical concordance with AI-generated diagnostic recommendations, using a validated questionnaire and a clinical scenario processed by ChatGPT.

Methods A national, cross-sectional web-based survey was conducted among 587 Italian physicians using an online validated questionnaire. The first part of the questionnaire assessed self-reported knowledge, prior experience, attitudes, and willingness to adopt AI in medicine. The second part assessed clinical concordance between AI proposals and physicians about clinical cases evaluated by ChatGPT (GPT-4). Descriptive and inferential statistics were used, including the Friedman test and Durbin-Conover post hoc comparisons.

Results Most participants reported basic AI knowledge (n=380, 64.5%) and minimal exposure to AI training (18.3%). The most familiar application was diagnostic imaging (47.0%). Only 22% reported they used AI in clinical practice, 82.0% expressed a willingness to adopt it, and 93.5% were interested in AI training. Major perceived barriers included lack of training (76.7%) and resistance to change (51.1%). In the universal clinical scenario, physicians showed the highest agreement with ChatGPT’s correct diagnosis (mean=4.06) compared to incorrect alternatives (mean=2.57 and 1.82, p<0.001). For Correct diagnosis, the agreement rate (agree or strongly agree) was very high at 89% [95%CI:86%-91%]. A total of 68% [95%CI:64%-72%] demonstrated diagnostic discrimination, both endorsing the correct diagnosis and rejecting the incorrect ones.

Conclusions Italian physicians showed a strong interest in adopting AI tools, despite significant knowledge gaps and limited practical experience. The high concordance between physicians’ evaluations and ChatGPT’s diagnostic insights suggests potential for AI-based decision support in clinical workflows. Targeted training and institutional support are essential to bridge the gap between enthusiasm and readiness for AI integration.

The authors have declared no competing interest.

This study was funded by University of L'Aquila, Department of Life, Health and Environmental Sciences (grant no. FFORIC25.18).

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