Artificial intelligence in cardiology: a narrative review with focus on patient outcomes

Abstract

Background and Objective: Artificial intelligence (AI) is rapidly transforming cardiology through advancements in diagnostic accuracy, prognostication, and treatment personalization. While evidence for algorithmic performance is robust, its true impact on patient-centered outcomes remains unclear. This review aims to evaluate how AI applications influence patient outcomes in cardiology and identify current limitations and future directions. Methods: A targeted literature search was conducted in PubMed, Scopus, Embase, and Cochrane databases on May 9 and 23, 2025, using a combination of terms related to AI, cardiology, and patient outcomes. Filters were applied to include human studies, English language, and studies published between January 2015 and May 2025. Two reviewers independently screened articles, and three reviewers reached consensus for final inclusion. A total of 11 studies met inclusion criteria. Key Content and Findings: AI tools have demonstrated potential benefits across multiple domains, including clinical decision support, cardiac imaging, remote patient monitoring, and patient engagement. Evidence suggests AI can enhance diagnostic accuracy, procedural efficiency, and patient self-management. However, most studies report surrogate or process-related endpoints rather than hard clinical outcomes. Large-scale randomized trials remain scarce, and improvements in mortality, hospitalization, and quality of life (QoL) are inconsistently demonstrated. Ethical considerations, implementation challenges, and cost-effectiveness concerns persist. Conclusions: AI in cardiology shows promise for improving patient care, but robust evidence linking its adoption to improved clinical outcomes is limited. By synthesizing available findings, this review highlights critical evidence gaps and provides guidance for future research, which should prioritize prospective trials focused on patient-centered endpoints and address barriers to implementation, transparency, and equity.

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