journal article·2025-11-22Open access

Augmenting, Not Replacing: Clinicians’ Perspectives on AI Adoption in Healthcare

Francesco Sensi, Francesca Lizzi, Andrea Chincarini, Chiara Binelli, Laura Sartori, Alessandra Retico

Applied Sciences· Volume 15· Issue 23

Abstract

Artificial intelligence (AI) is widely expected to transform healthcare, yet its adoption in clinical practice remains limited. This paper examines the perspectives of Italian clinicians and medical physicists on the drivers of and barriers to AI use. Using an online survey of healthcare professionals across different domains, we find that efficiency gains—such as reducing repetitive tasks and accelerating diagnostics—are the strongest incentives for adoption. However, trust in AI systems, explainability, and the limited availability of AI tools are major obstacles. Respondents emphasized that AI should augment, not replace, medical expertise, calling for participatory development processes where clinicians are actively involved in the design and validation of decision support tools. At the organizational level, the adoption of AI tools is facilitated by innovation-oriented leadership and sufficient resources, while conservative management and economic constraints hinder implementation. The awareness of regulatory frameworks, including the EU AI Act, is moderate, and many clinicians express the need for targeted training to support safe integration. Our findings suggest that the successful adoption of AI in healthcare will depend on building trust through transparency, clarifying legal responsibilities, and fostering organizational cultures that support collaboration between humans and AI. The role of AI in medicine is therefore best understood as a complement to clinical judgment, rather than a replacement.
Source: crossref · Quality: baseline

Research context

OpenAlex topics: Artificial Intelligence in Healthcare and Education · Ethics and Social Impacts of AI · Electronic Health Records Systems

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

Genova Division, National Institute for Nuclear Physics (INFN), 16146 Genova, Italy

Pisa Division, National Institute for Nuclear Physics (INFN), 56127 Pisa, Italy

Department of Political and Social Sciences, University of Bologna, 40125 Bologna, Italy

Funding

European Commission · INFN-CSN5

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