Closing the Artificial Intelligence Implementation Gap for Heart Failure Care: How Recent Science Shapes the Next Steps.

Abstract

Despite major advances in the treatment and monitoring of heart failure, many patients are still diagnosed late, incompletely phenotyped, and insufficiently managed over time. Artificial intelligence (AI) is well positioned to address these gaps, yet recent studies show that strong algorithmic performance does not reliably translate into measurable clinical benefit. We draw on recent prospective evidence to examine this AI implementation gap and argue that successful AI implementation does not depend on model performance but rather on how models are designed, evaluated, embedded in clinical workflows and underpinned by governance structures that ensure responsible use. We propose a reorientation across 4 fronts. First, AI models should be designed backward from clinical decisions, not optimized for benchmark performance. Second, deployment must shift from unselected, low-prevalence populations to settings where pretest probability supports clinical utility and model outputs are embedded in actionable workflows. Third, evidence generation should include independent validation to reduce developer bias, simulation-based validation to approximate clinical reality, and prospective trials with meaningful clinical outcomes. Fourth, governance frameworks should shift from model-level oversight to system-level accountability, with defined responsibility across clinicians, institutions, and developers.