Key result
AI-driven interventions cost-effectively improve cardiac disease detection and reduce adverse events despite EHR integration barriers.
Why the study?
There is a critical gap between the theoretical benefits of AI in cardiac care and its practical deployment, with challenges in implementation and economic evaluation.
Do artificial intelligence-driven interventions improve clinical outcomes and cost-effectiveness compared to standard care in cardiac care?
Systematic Review (n=14)
Do artificial intelligence-driven interventions improve clinical outcomes and cost-effectiveness compared to standard care in cardiac care?
AI-driven interventions in cardiology improve disease detection and clinical outcomes cost-effectively, though real-world implementation faces significant workflow and integration barriers.
Supports AI adoption in cardiac diagnostics and monitoring; extends evidence on clinical and economic benefits while noting implementation gaps.
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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Zadeh et al. (2025) conducted a systematic review in Cardiac care (n=14). Artificial Intelligence (AI) solutions vs. Standard care was evaluated on Economic impact, clinical outcomes, and implementation challenges. AI-driven interventions in cardiac care improve disease detection rates and reduce adverse events while proving cost-effective, though real-world implementation faces barriers like EHR integration costs.
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