Antonin, T., Stéphane, L., Floran, B., Gauthier, B., Orianne, W., Paul, L., Nabil, B., Thierry, G., Marc, V., Jérémie, B., Cyril, F., & Louis-Marie, D. (2026). 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.. Archives of cardiovascular diseases. https://doi.org/10.1016/j.acvd.2025.06.078
Antonin T, Stéphane L, Floran B, Gauthier B, Orianne W, Paul L, et al. 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.. Archives of cardiovascular diseases. 2026; doi: 10.1016/j.acvd.2025.06.078
Antonin T, Stéphane L, Floran B, et al. 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.[J]. Archives of cardiovascular diseases. 2026. DOI: 10.1016/j.acvd.2025.06.078.
@article{antonin2026,
author = {Trimaille Antonin and Lafitte Stéphane and Begue Floran and Beuque Gauthier and Weizman Orianne and Lucain Paul and Bouali Nabil and Garban Thierry and Villaceque Marc and Barraud Jérémie and Ferdynus Cyril and Desroche Louis-Marie},
title = {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.},
journal = {Archives of cardiovascular diseases},
year = {2026},
doi = {10.1016/j.acvd.2025.06.078},
note = {PMID: 40925795},
}
TY - JOUR AU - Trimaille Antonin AU - Lafitte Stéphane AU - Begue Floran AU - Beuque Gauthier AU - Weizman Orianne AU - Lucain Paul AU - Bouali Nabil AU - Garban Thierry AU - Villaceque Marc AU - Barraud Jérémie AU - Ferdynus Cyril AU - Desroche Louis-Marie TI - 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. T2 - Archives of cardiovascular diseases PY - 2026 DO - 10.1016/j.acvd.2025.06.078 AN - PMID:40925795 ER -
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.