Artificial Intelligence in Healthcare: A Scoping Review of Medical Professionals' Acceptance and Institutional...

Artificial intelligence (AI) is transforming healthcare at a fast pace, showing promising potential to enhance medical diagnosis, inform treatment strategies, and support patient care. These advancements have the potential to improve clinical outcomes, streamline workflows, and reduce errors. However, comprehending the level of acceptance among medical professionals and the institutional challenges involved in implementing AI is essential.

This scoping review aimed to identify the acceptance of AI among medical professionals and to identify the institutional barriers that impede its widespread implementation.

A scoping review methodology was applied to analyze studies published between 2015 and 2025. The review included peer-reviewed articles focusing on medical professionals' perspectives on AI adoption, including factors like acceptance, attitudes, benefits, and challenges. Key databases such as PubMed, Scopus, and IEEE Xplore were searched to ensure comprehensive coverage of relevant research. Data were extracted and categorized into themes related to AI acceptance, barriers, and institutional challenges.

Two major themes emerged: (1) medical professionals' acceptance of AI and (2) institutional challenges to implementation. AI tools used in diagnostic imaging, administrative support, and natural language processing were generally well accepted due to perceived efficiency and accuracy. Conversely, predictive models and clinical decision support systems received cautious responses, primarily due to concerns about interpretability, trust, and autonomy. Institutional barriers included limited infrastructure, lack of integration with existing health records, financial constraints, inadequate training opportunities, and regulatory ambiguities regarding liability, privacy, and fairness.

While AI holds transformative potential for healthcare, its successful adoption requires addressing both human and systemic factors. Enhancing AI literacy, investing in infrastructure, and developing clear regulatory guidelines are critical to overcoming resistance and enabling meaningful integration.