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Publications

Journal Article Epublish

PubMed: 42077381 DOI: 10.1093/ehjdh/ztag042

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

Eur Heart J Digit Health 7(4):ztag042

Authors: Nabil Bouali, Séverine Domart, Walid Amara, Christophe Laure, Floran Begue, Johann Reisberg, Thierry Garban, Guillaume Bailly, Simon Viscogliosi, Chaimae Aboueddahab, Soundous M'Rabet, Sébastien Hascoet, Guillaume Baudry, Manveer Singh, Youssef Lakhal, Paul Lucain, Gauthier Beuque, Philippe Régnier, Antonin Trimaille, Marc Villaceque, Orianne Weizman, Jérémie Barraud, Stéphane Lafitte, Cyril Ferdynus, Louis-Marie Desroche

Affiliations

  • Cardiology Department, University Hospital of Poitiers, 2 rue de la Milétrie, CS 90577, Poitiers CEDEX 86021, France.
  • Department of Cardiology, University Hospital of La Réunion - Saint-Denis, North Site CS 11021, Allée des Topazes, 97400 Saint-Denis, La Réunion, France.
  • Rhythmology Unit, Intermunicipal Hospital Group Le Raincy Montfermeil, Montfermeil, France.
  • Clinical Research and Innovation Directorate, Chartres Hospital Center, Le Coudray, France.
  • USMD, Clinical Research and Innovation Unit (DRCI), University Hospital of La Réunion, Saint-Pierre, France.
  • Cardiology Department, Pitié-Salpêtrière Hospital, Assistance Publique-Hôpitaux de Paris (AP-HP), Paris, France.
  • Department of Cardiology, National Union of Cardiologists, Paris, France.
  • Cardiology Department, Saint-Antoine and Tenon Hospitals, AP-HP, Sorbonne University, Paris, France.
  • Sorbonne University, AP-HP, Inserm CIC-1901, Department of Pharmacology, Pitié-Salpêtrière Hospital, Paris, France.
  • Department of Cardiology, Hospices Civils de Lyon, Lyon, France.
  • Cardiology Department, Ibn Sina University Hospital, Mohammed V University of Rabat, Rabat, Morocco.
  • Department of Cardiovascular Medicine, Nouvel Hôpital Civil, Strasbourg University Hospital, Strasbourg, France.
  • Cardiology Department, Dijon University Hospital, Dijon, France.
  • Pediatric and Congenital Cardiology Working Group (FCPC), French Society of Cardiology, Paris, France.
  • Department of Congenital Heart Diseases, Marie Lannelongue Hospital, M3C Network, Le Plessis-Robinson, France.
  • Department of Medicine, Paris-Saclay University, Kremlin-Bicêtre School of Medicine, Le Kremlin-Bicêtre, Paris, France.
  • Hypertension and Therapeutic Innovation Unit, INSERM UMR-S 999, Le Plessis-Robinson, France.
  • Université de Lorraine, Inserm CIC-1433, Inserm U1116, Nancy University Hospital, Nancy, France.
  • INI-CRCT (Cardiovascular and Renal Clinical Trialists), F-CRIN Network, Nancy, France.
  • REICATRA, Université de Lorraine, Vandœuvre-lès-Nancy, France.
  • Cardiology Department, Hôpital Lariboisière, AP-HP, Paris, France.
  • Faculty of Medicine, Université Paris Cité, Paris, France.
  • Cardiology Department, Amiens University Hospital, Amiens, France.
  • Cardiology Department, Bordeaux University Hospital, Bordeaux, France.
  • Cardiology Department, Paul d'Égine Private Hospital, Champigny-sur-Marne, France.
  • Department of Cardiovascular Medicine, Strasbourg University Hospital, Strasbourg, France.
  • Private Practice, Cabinet de Cardiologie Libérale, Nîmes, France.
  • Cardiology Department, Clinique Claude Bernard, Metz, France.
  • Cardiology Department, Aix-en-Provence Hospital, Aix-en-Provence, France.
  • CIC, Centre d'Investigation Clinique - Epidémiologie Clinique (CIC-EC) University Hospital of La Réunion - Saint-Denis, North Site CS 11021, Allée des Topazes, 97400 Saint-Denis, La Réunion, France.

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.

MeSH

Intelligence, Cardiology, Cardiologists, Artificial Intelligence, Education