ReviewCureus2025
The Role of Artificial Intelligence in Improving Diagnosis, Management, and Outcomes of Acute Myocardial Ischemia: A Systematic Review.
Review in Cureus, 2025. 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
15 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence (AI) has emerged as a transformative force in cardiovascular medicine, particularly in the diagnosis, management, and prognostication of acute myocardial ischemia (AMI). This systematic review synthesizes current evidence on AI applications across diagnostic modalities, risk stratification, therapeutic decision-making, and outcome prediction in AMI. A total of 30 peer-reviewed studies were included, encompassing machine learning (ML), deep learning (DL), and hybrid models applied to electrocardiography (ECG), imaging, and electronic health records (EHRs). AI demonstrated superior diagnostic accuracy, enhanced triage efficiency, and improved prognostic modeling compared to conventional methods. Notably, AI-enabled ECG interpretation and coronary imaging have shown cardiologist-level performance in detecting ischemia. Risk prediction models using ML have outperformed traditional scoring systems, while AI-driven decision support tools have optimized therapeutic pathways. Despite promising results, challenges remain in clinical integration, interpretability, and generalizability. This review underscores the potential of AI to revolutionize AMI care and highlights future directions for research, validation, and ethical implementation.
Indexed as
Identifiers
What Socratic holds
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.