Artificial intelligence adoption in French cardiovascular care: a multiprofessional survey of barriers and facilitators

Aims: Responsible adoption of artificial intelligence (AI) in cardiology remains uneven. We aimed to map knowledge, attitudes, beliefs and practices among cardiovascular professionals in France and to identify levers for implementation. Methods and results: We conducted a national multiprofessional survey across cardiovascular care from 4 December 2024 to 1 March 2025. Prespecified outcomes included regular use in practice, confidence in diagnostic outputs, performance expectations, training needs, and social influence. Seven hundred fifty-six professionals completed the survey (58.2% cardiologists, 24.3% allied-health professionals, 17.8% other professionals; median age 37 years; 46.7% women). AI use was reported as regular (≥ weekly) by 23%, occasionally by 40%, and none by 37%; only 7.8% had formal AI training. Use concentrated on AI-assisted imaging (32%) and patient monitoring/management (18%). The most valued benefit was improved diagnostic accuracy (29%); leading concerns were algorithmic bias (29.9%) and data privacy (28.2%). Explainability increased confidence (among cardiologists, high confidence 64% in therapeutic contexts vs. 84% with explanations). In multivariable analyses, prior training (aOR 3.22, 95% CI 1.60-6.55), research involvement (2.94, 1.90-4.58), and male sex (1.64, 1.05-2.59) were associated with higher use, while age > 40 years was associated with lower use (0.62, 0.40-0.96). Allied-health professionals reported lower social influence and training needs. Conclusion: Adoption of AI in cardiology remains limited, and four levers emerged for responsible scale-up: Training (education), Explainability (transparent outputs), Integration (workflow embedding), and Accompaniment (peer support, evaluation). These priorities should guide education, governance, and procurement strategies.

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Publication Details

Journal
European Heart Journal - Digital Health
Published
2026-04-10
DOI
https://doi.org/10.1093/ehjdh/ztag042
Primary Topic
Artificial Intelligence in Healthcare and Education
Type
article
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article

Artificial intelligence adoption in French cardiovascular care: a multiprofessional survey of barriers and facilitators

Sébastien Hascoët, Soundous M’Rabet, Floran Bègue, Louis‐Marie Desroche et al.
European Heart Journal - Digital Health
Artificial Intelligence in Healthcare and Education
article

Artificial intelligence adoption in French cardiovascular care: a multiprofessional survey of barriers and facilitators

Sébastien Hascoët, Soundous M’Rabet, Floran Bègue, Louis‐Marie Desroche, Cyril Ferdynus, J. Barraud, Marc Villacèque, Stéphane Lafitte, Thierry Garban, Antonin Trimaille, Orianne Weizman, Nabil Bouali, Guillaume Baudry, Simon Viscogliosi, chaimae aboueddahab, Dr Walid Amara, Christophe Laure, Manveer Singh, Gauthier Beuque, Paul Lucain, Johann Reisberg, Séverine Domart, Philippe Régnier, Youssef Lakhal, Guillaume Bailly
article en

Abstract

Aims: Responsible adoption of artificial intelligence (AI) in cardiology remains uneven. We aimed to map knowledge, attitudes, beliefs and practices among cardiovascular professionals in France and to identify levers for implementation. Methods and results: We conducted a national multiprofessional survey across cardiovascular care from 4 December 2024 to 1 March 2025. Prespecified outcomes included regular use in practice, confidence in diagnostic outputs, performance expectations, training needs, and social influence. Seven hundred fifty-six professionals completed the survey (58.2% cardiologists, 24.3% allied-health professionals, 17.8% other professionals; median age 37 years; 46.7% women). AI use was reported as regular (≥ weekly) by 23%, occasionally by 40%, and none by 37%; only 7.8% had formal AI training. Use concentrated on AI-assisted imaging (32%) and patient monitoring/management (18%). The most valued benefit was improved diagnostic accuracy (29%); leading concerns were algorithmic bias (29.9%) and data privacy (28.2%). Explainability increased confidence (among cardiologists, high confidence 64% in therapeutic contexts vs. 84% with explanations). In multivariable analyses, prior training (aOR 3.22, 95% CI 1.60-6.55), research involvement (2.94, 1.90-4.58), and male sex (1.64, 1.05-2.59) were associated with higher use, while age > 40 years was associated with lower use (0.62, 0.40-0.96). Allied-health professionals reported lower social influence and training needs. Conclusion: Adoption of AI in cardiology remains limited, and four levers emerged for responsible scale-up: Training (education), Explainability (transparent outputs), Integration (workflow embedding), and Accompaniment (peer support, evaluation). These priorities should guide education, governance, and procurement strategies.

European Heart Journal - Digital HealthVol. 7(4)
Mohammed V University (MA), Université de Bordeaux (FR), Inserm (FR), Université Paris Cité (FR), University of Reunion Island (RE), Université Paris-Saclay (FR), Université de Poitiers (FR), Sorbonne Université (FR), Centre Hospitalier Universitaire de La Réunion (RE), Sorbonne Paris Cité (FR), Centre Hospitalier Universitaire Amiens-Picardie (FR), Assistance Publique – Hôpitaux de Paris (FR), Hospices Civils de Lyon (FR), Bordeaux Population Health (FR), Hypertension pulmonaire : physiopathologie et innovation thérapeutique (FR), Centre Hospitalier Intercommunal Aix-Pertuis (FR), Centre Hospitalier Universitaire de Poitiers (FR), Les Hôpitaux de Chartres (FR), Hôpital Clinique Claude-Bernard (FR), Hôpital Lariboisière (FR), Hôpital Marie Lannelongue (FR), Pitié-Salpêtrière Hospital (FR), International Union Against Tuberculosis and Lung Disease (FR), Centre de Recherche Saint-Antoine (FR), Peuplements végétaux et bioagresseurs en milieu tropical (RE), Délégation Nord, Pas-de-Calais et Picardie (FR), Société Française de Cardiologie (FR), Maison des Sciences sociales et des Humanités de Dijon (FR), Ibn Sina Hospital (KW), Groupe Hospitalier Intercommunal Le Raincy Montfermeil (FR), Bicêtre Hospital (FR), Hôpital Civil, Strasbourg (FR), Hôpital Tenon (FR), Easy Global Market (France) (FR), French Clinical Research Infrastructure Network (FR), Université de Strasbourg (FR), Université de Lorraine (FR)
Openalex Percentile: Top 21%
Artificial Intelligence in Healthcare and Education
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