ReviewCardiovascular diagnosis and therapy2026
Artificial intelligence in cardiology: a narrative review with focus on patient outcomes.
Review in Cardiovascular diagnosis and therapy, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.