Shyon Parsa, Sulaiman Somani, Ramzi Dudum, Sneha S Jain, Fatima Rodriguez
{"title":"人工智能在心血管疾病预防中的应用：它准备好进入黄金时代了吗？","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":"{\"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}","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
摘要
综述目的：本综述评估了人工智能（AI）如何通过分析非结构化临床数据和患者生成的数据，加强动脉粥样硬化性心血管疾病（ASCVD）风险评估，实现机会性筛查，并改善指南的遵循情况。此外，它还讨论了将人工智能融入预防心脏病学临床实践的策略：与传统的风险评分相比，人工智能模型在个性化 ASCVD 风险评估中表现出了卓越的性能。这些模型目前支持自动检测各种成像模式的 ASCVD 风险指标，包括冠状动脉钙化（CAC），如专用心电图门控 CT 扫描、胸部 X 光片、乳房 X 光片、冠状动脉造影术和非门控胸部 CT 扫描。此外，大型语言模型（LLM）管道可有效识别和解决 ASCVD 预防保健中的差距和差异，还能加强患者教育。事实证明，人工智能应用在预防和管理急性心血管疾病方面非常有价值，只要在规范、迭代的临床路径中加以实施，就可以在临床上使用。
Artificial Intelligence in Cardiovascular Disease Prevention: Is it Ready for Prime Time?
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.
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号
