Shyon Parsa, Sulaiman Somani, Ramzi Dudum, Sneha S Jain, Fatima Rodriguez
{"title":"Artificial Intelligence in Cardiovascular Disease Prevention: Is it Ready for Prime Time?","authors":"Shyon Parsa, Sulaiman Somani, Ramzi Dudum, Sneha S Jain, Fatima Rodriguez","doi":"10.1007/s11883-024-01210-w","DOIUrl":null,"url":null,"abstract":"<p><strong>Purpose of review: </strong>This review evaluates how Artificial Intelligence (AI) enhances atherosclerotic cardiovascular disease (ASCVD) risk assessment, allows for opportunistic screening, and improves adherence to guidelines through the analysis of unstructured clinical data and patient-generated data. Additionally, it discusses strategies for integrating AI into clinical practice in preventive cardiology.</p><p><strong>Recent findings: </strong>AI models have shown superior performance in personalized ASCVD risk evaluations compared to traditional risk scores. These models now support automated detection of ASCVD risk markers, including coronary artery calcium (CAC), across various imaging modalities such as dedicated ECG-gated CT scans, chest X-rays, mammograms, coronary angiography, and non-gated chest CT scans. Moreover, large language model (LLM) pipelines are effective in identifying and addressing gaps and disparities in ASCVD preventive care, and can also enhance patient education. AI applications are proving invaluable in preventing and managing ASCVD and are primed for clinical use, provided they are implemented within well-regulated, iterative clinical pathways.</p>","PeriodicalId":10875,"journal":{"name":"Current Atherosclerosis Reports","volume":" ","pages":"263-272"},"PeriodicalIF":5.7000,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11457745/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Current Atherosclerosis Reports","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1007/s11883-024-01210-w","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/5/23 0:00:00","PubModel":"Epub","JCR":"Q1","JCRName":"PERIPHERAL VASCULAR DISEASE","Score":null,"Total":0}
引用次数: 0
Abstract
Purpose of review: This review evaluates how Artificial Intelligence (AI) enhances atherosclerotic cardiovascular disease (ASCVD) risk assessment, allows for opportunistic screening, and improves adherence to guidelines through the analysis of unstructured clinical data and patient-generated data. Additionally, it discusses strategies for integrating AI into clinical practice in preventive cardiology.
Recent findings: AI models have shown superior performance in personalized ASCVD risk evaluations compared to traditional risk scores. These models now support automated detection of ASCVD risk markers, including coronary artery calcium (CAC), across various imaging modalities such as dedicated ECG-gated CT scans, chest X-rays, mammograms, coronary angiography, and non-gated chest CT scans. Moreover, large language model (LLM) pipelines are effective in identifying and addressing gaps and disparities in ASCVD preventive care, and can also enhance patient education. AI applications are proving invaluable in preventing and managing ASCVD and are primed for clinical use, provided they are implemented within well-regulated, iterative clinical pathways.
期刊介绍:
The aim of this journal is to systematically provide expert views on current basic science and clinical advances in the field of atherosclerosis and highlight the most important developments likely to transform the field of cardiovascular prevention, diagnosis, and treatment.
We accomplish this aim by appointing major authorities to serve as Section Editors who select leading experts from around the world to provide definitive reviews on key topics and papers published in the past year. We also provide supplementary reviews and commentaries from well-known figures in the field. An Editorial Board of internationally diverse members suggests topics of special interest to their country/region and ensures that topics are current and include emerging research.