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Heart failure patients trust their cardiologists more than artificial intelligence (AI). While some support AI assistance, most prefer human oversight for diagnosis and treatment decisions, especially in complex care.

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Area of Science:

  • Cardiology
  • Medical Informatics
  • Artificial Intelligence in Healthcare

Background:

  • Artificial intelligence (AI) tools are emerging for chronic disease management, including heart failure (HF).
  • Limited understanding exists regarding HF patient perspectives on AI integration into their care.
  • This study addresses the need to explore patient views for patient-centered AI adoption in HF.

Purpose of the Study:

  • To investigate heart failure patients' perceptions of integrating AI into their care.
  • To guide the patient-centered adoption of AI in heart failure management.
  • To assess patient attitudes towards AI in diagnosis and treatment decisions.

Main Methods:

  • A cross-sectional survey was administered to 110 patients with heart failure with reduced ejection fraction.
  • Questionnaires assessed satisfaction with current care, trust in cardiologists, and attitudes toward AI involvement.
  • Data were analyzed using logistic regression, examining associations with demographics, smartphone ownership, and symptom burden.

Main Results:

  • Patient attitudes towards AI in HF care were mixed; only 38.1% supported AI in treatment decisions with physician input.
  • Support significantly decreased when AI acted independently (18.2%) or managed remote adjustments (21.8%).
  • A strong preference for cardiologist diagnoses (80.9%) and treatment plans (84.6%) was observed, with 97.3% trusting cardiologists over AI in disagreements. Smartphone ownership correlated with greater acceptance of remote AI adjustments.

Conclusions:

  • Heart failure patients express high satisfaction with current care and strong trust in cardiologists, showing more caution towards AI than general populations.
  • Smartphone ownership, not demographics or prior AI experience, predicted openness to AI-driven remote adjustments.
  • Equitable AI adoption in HF care hinges on maintaining clinician oversight and developing accessible AI tools.