Bias, External Validation, and Real-World Implementation of Artificial Intelligence Models in Cardiovascular Medicine

Artificial intelligence (AI) and machine learning (ML) have demonstrated strong diagnostic and prognostic performance across cardiovascular medicine. However, translation into equitable real-world benefit is limited by algorithmic bias, inadequate external validation, and unclear implementation pathways. This State-of-the-Art Review evaluates these challenges using the Total Product Life Cycle (TPLC) framework, encompassing development, validation, regulatory approval, deployment, and post-market surveillance. We synthesize current literature on bias mechanisms, validation strategies, and real-world implementation, and critically assess emerging technical solutions, including federated learning and explainable AI. Bias enters at multiple lifecycle stages through unrepresentative data, flawed labels, measurement variability, and deployment mismatch. Most cardiovascular AI models rely on limited external validation, often lacking geographic or domain generalizability. A 2025 analysis of 691 FDA-cleared AI/ML devices showed 95.5% lacked demographic transparency and only 1.6% had randomized trial evidence. Implementation barriers include dataset shift, regulatory gaps, and inequitable access, with limited prospective outcome data supporting clinical benefit. Current cardiovascular AI deployment is not matched by sufficient evidence for safety, equity, and effectiveness. A TPLC-aligned framework with rigorous validation, demographic transparency, and continuous post-market monitoring is essential to ensure equitable and clinically meaningful integration of AI into cardiovascular care.