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. 2025 Sep 2:S1875-2136(25)00444-9.
doi: 10.1016/j.acvd.2025.06.078. Online ahead of print.

Unveiling facilitators and barriers to artificial intelligence implementation in cardiac healthcare: Rationale and design of the INSIGHT-AI France study, from the Artificial Intelligence Working Group and the National College of Cardiologists in Training of the French Society of Cardiology

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Unveiling facilitators and barriers to artificial intelligence implementation in cardiac healthcare: Rationale and design of the INSIGHT-AI France study, from the Artificial Intelligence Working Group and the National College of Cardiologists in Training of the French Society of Cardiology

Antonin Trimaille et al. Arch Cardiovasc Dis. .

Abstract

Background: Artificial intelligence has emerged as a promising tool to optimize patient care in the field of cardiovascular medicine. However, data on its adoption and utilization by healthcare professionals are scarce.

Aim: To explore the factors that support or hinder the adoption of artificial intelligence in cardiology in France.

Methods: The INSIGHT-AI France study is a two-wave longitudinal panel survey recontacting the same individuals after 12 months, targeting professionals involved in the management of patients with cardiovascular diseases, including senior cardiologists, residents, nurses, technicians, engineers and decision-makers involved in artificial intelligence development. Participants from academic, public non-academic and private hospitals were recruited using a stratified sampling approach to capture diverse perspectives. Data were collected via SKEZIA, a platform compliant with the General Data Protection Regulation, with secure authentication and longitudinal tracking capabilities. The baseline survey, distributed from December 2024 to March 2025, assessed knowledge, attitudes, beliefs and practices related to artificial intelligence in cardiology. A follow-up survey will be conducted 12 months later to evaluate changes over time. The survey was developed by a scientific committee, with feedback from artificial intelligence and cardiology experts, and was pilot-tested for feasibility. Statistical analyses will include mixed-effects models and regression analyses.

Conclusions: This is the first study designed to explore the acceptance and limitation of artificial intelligence use in cardiovascular medicine in France. By identifying key facilitators and barriers, this study aims to inform strategic initiatives for more effective and equitable artificial intelligence implementation in French-speaking healthcare systems.

Keywords: Artificial intelligence; Cardiology; Healthcare professionals; Longitudinal study; Survey.

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Conflict of interest statement

Disclosure of interest A.T. has received a grant from Edwards. The other authors declare that they have no competing interest.

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