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Bridging the Knowledge, Usage, and Regulation Gap for Artificial Intelligence in Medicine: A Cross-Sectional Survey of Spanish Clinicians and Trainees

Aug 2026 · Journal of Scientific Innovation in Medicine · 0 citations · 64 references

TL;DR

Findings reveal significant educational, generational, and gender gaps that may hinder AI adoption in clinical practice and strengthen interdisciplinary collaboration, promoting inclusive AI education, and involving clinicians in regulatory processes are essential to ensure responsible, equitable, and effective integration of AI in healthcare.

Abstract

Objective: Artificial intelligence (AI) is rapidly transforming healthcare, offering opportunities to improve diagnostics, optimize workflows, and reduce medical errors. Effective integration requires not only technological innovation but also clinician engagement and education. This study explores the perceptions, knowledge, and experiences of Spanish medical professionals and students regarding AI in healthcare. Methods: A cross-sectional online survey was conducted between October and December 2024, yielding 167 valid responses from diverse medical backgrounds. Results: Participants reported higher familiarity with general technology (mean = 6.6/10) than with AI (5.0) or AI in medicine (4.2). Younger respondents demonstrated greater AI literacy but less experience with medical software. Gender disparities were evident, with males reporting significantly higher knowledge and engagement across all domains. Although 95.8% of participants recognized ChatGPT, familiarity with medical AI tools was minimal. Respondents expressed limited awareness of AI regulation (mean = 2.2/10) and uncertainty about physicians’ roles in policy-making, despite broad support for ethical oversight. Conclusions: These findings reveal significant educational, generational, and gender gaps that may hinder AI adoption in clinical practice. Strengthening interdisciplinary collaboration, promoting inclusive AI education, and involving clinicians in regulatory processes are essential to ensure responsible, equitable, and effective integration of AI in healthcare.

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