Economic Impact, Clinical Outcomes, and Implementation Challenges of Artificial Intelligence Solutions in Cardiac Care: A Systematic Literature Review

Abstract

Artificial intelligence (AI) is transforming cardiac care by enhancing diagnosis, risk stratification, and patient monitoring. This systematic review synthesizes evidence from 14 studies (2020–2025) on the economic impact, clinical outcomes, and implementation challenges of AI in cardiology. Findings demonstrate that AI-driven interventions—including machine learning-guided atrial fibrillation screening, AI-enhanced cardiac imaging, and remote monitoring—improve disease detection rates and reduce adverse events (e.g., strokes, hospitalizations) while proving cost-effective. For instance, targeted AI screening identified 27–45% more atrial fibrillation cases versus standard care, with incremental cost-effectiveness ratios favoring AI adoption. However, real-world implementation faces barriers such as electronic health record integration costs, clinician adoption resistance, and workflow disruptions. Facilitators like phased rollouts, embedded decision support tools, and real-world performance tracking mitigated these challenges. Methodological limitations include study heterogeneity, reliance on model-based economic analyses, and underreporting of long-term implementation costs. The review highlights a critical gap between AI's theoretical benefits and its practical deployment, emphasizing the need for pragmatic trials, standardized outcome reporting, and stakeholder collaboration. By addressing these challenges, AI can realize its potential to enhance cardiac care efficiency and patient outcomes.

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