ReviewHeart views : the official journal of the Gulf Heart Association
Harnessing Artificial Intelligence in Cardiology: Advancements in Diagnosis, Treatment, and Patient Care.
Review in Heart views : the official journal of the Gulf Heart Association. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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
6 citing papers in PubMed.
- Wearable Devices in Cardiovascular Care: A Narrative Review of the Transition Toward Predictive, Preventive, Personalized, and Participatory Medicine.Healthcare (Basel, Switzerland) · 2026Review
- Explainable and Trustworthy Artificial Intelligence in Cardiology: A Narrative Review of Clinical Applications, Operational Integration, and Future Directions.Journal of clinical medicine · 2026Review
- A leap into the future: excluding the ischaemic origin of chest pain through artificial intelligence.European heart journal supplements : journal of the European Society of Cardiology · 2026Article
- Artificial intelligence in cardiology in the current era: a narrative review.Cardiovascular diagnosis and therapy · 2026Review
- Artificial intelligence-based approaches for monitoring medication adherence among cardiovascular disease patients: a scoping review.BMC medical informatics and decision making · 2026Article
- Unsupervised machine learning for cardiovascular disease: A framework for future studies.European journal of heart failure · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Heart disease remains a leading cause of morbidity and mortality globally, straining healthcare systems worldwide. Artificial intelligence (AI) offers transformative solutions in cardiology, enhancing early diagnosis, personalized treatment, and surgical precision. This review examines the impact of AI on cardiology, focusing on its role in analyzing medical images, predicting risk, and managing chronic diseases. AI algorithms and intense learning models have demonstrated exceptional proficiency in interpreting imaging modalities, surpassing human diagnostic capabilities. Predictive analytics models utilizing electronic health records, genomic data, and lifestyle information significantly improve early detection and risk prediction of heart conditions. In addition, AI facilitates personalized treatment plans by predicting patient responses to specific medications and adjusting therapies in real time. In chronic disease management, AI-powered wearable devices and telehealth platforms enable continuous monitoring and timely interventions, reducing hospital readmissions. Furthermore, AI enhances surgical outcomes through real-time assistance and precision. While AI's potential in cardiology is vast, ethical considerations and challenges such as data privacy, bias, and the "black box" nature of some algorithms must be addressed. AI stands poised to revolutionize cardiovascular medicine, offering promising avenues for improved patient outcomes and healthcare efficiency.
Indexed as
Identifiers
What OpenQuestion 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 OpenQuestion graph.