The Role of Artificial Intelligence in Improving Patient Outcomes and Future of Healthcare Delivery in Cardiology: A Narrative Review of the Literature

被引：45
作者：
Gala, Dhir [1 ]
Behl, Haditya [1 ]
Shah, Mili [1 ]
Makaryus, Amgad N. [2 ,3 ]
机构：
[1] Amer Univ Caribbean Sch Med, Dept Clin Sci, Cupecoy, Sint Maarten, Netherlands
[2] Hofstra Univ, Donald & Barbara Zucker Sch Med Hofstra Northwell, 500 Hofstra Blvd, Hempstead, NY 11549 USA
[3] Nassau Univ, Med Ctr, Dept Cardiol, Hempstead, NY 11554 USA
来源：
HEALTHCARE | 2024年 / 12卷 / 04期
关键词：
cardiovascular diseases; artificial intelligence; cardiology; clinical decision support systems; patient outcomes; predictive models; personalized care; physician efficiency; DECISION-SUPPORT-SYSTEMS; CORONARY-HEART-DISEASE; SECONDARY PREVENTION; RISK; PREDICTION; DIAGNOSIS; DOCUMENTATION; TECHNOLOGY; STRATEGIES; MANAGEMENT;
D O I：
10.3390/healthcare12040481
中图分类号：
R19 [保健组织与事业（卫生事业管理）];
学科分类号：
摘要：
Cardiovascular diseases exert a significant burden on the healthcare system worldwide. This narrative literature review discusses the role of artificial intelligence (AI) in the field of cardiology. AI has the potential to assist healthcare professionals in several ways, such as diagnosing pathologies, guiding treatments, and monitoring patients, which can lead to improved patient outcomes and a more efficient healthcare system. Moreover, clinical decision support systems in cardiology have improved significantly over the past decade. The addition of AI to these clinical decision support systems can improve patient outcomes by processing large amounts of data, identifying subtle associations, and providing a timely, evidence-based recommendation to healthcare professionals. Lastly, the application of AI allows for personalized care by utilizing predictive models and generating patient-specific treatment plans. However, there are several challenges associated with the use of AI in healthcare. The application of AI in healthcare comes with significant cost and ethical considerations. Despite these challenges, AI will be an integral part of healthcare delivery in the near future, leading to personalized patient care, improved physician efficiency, and anticipated better outcomes.
引用
收藏
页数：19
相关论文
共 84 条
[1]   The impact of artificial intelligence in medicine on the future role of the physician [J].
Ahuja, Abhimanyu S. .
PEERJ, 2019, 7
[2]  
Ajami S, 2016, NATL MED J INDIA, V29, P148
[3]   Automation in nursing decision support systems: A systematic review of effects on decision making, care delivery, and patient outcomes [J].
Akbar, Saba ;
Lyell, David ;
Magrabi, Farah .
JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION, 2021, 28 (11) :2502-2513
[4]   Revolutionizing healthcare: the role of artificial intelligence in clinical practice [J].
Alowais, Shuroug A. ;
Alghamdi, Sahar S. ;
Alsuhebany, Nada ;
Alqahtani, Tariq ;
Alshaya, Abdulrahman I. ;
Almohareb, Sumaya N. ;
Aldairem, Atheer ;
Alrashed, Mohammed ;
Bin Saleh, Khalid ;
Badreldin, Hisham A. ;
Al Yami, Majed S. ;
Al Harbi, Shmeylan ;
Albekairy, Abdulkareem M. .
BMC MEDICAL EDUCATION, 2023, 23 (01)
[5]   To explain or not to explain?-Artificial intelligence explainability in clinical decision support systems [J].
Amann, Julia ;
Vetter, Dennis ;
Blomberg, Stig Nikolaj ;
Christensen, Helle Collatz ;
Coffee, Megan ;
Gerke, Sara ;
Gilbert, Thomas K. ;
Hagendorff, Thilo ;
Holm, Sune ;
Livne, Michelle ;
Spezzatti, Andy ;
Strumke, Inga ;
Zicari, Roberto, V ;
Madai, Vince Istvan .
PLOS DIGITAL HEALTH, 2022, 1 (02)
[6]   End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography [J].
Ardila, Diego ;
Kiraly, Atilla P. ;
Bharadwaj, Sujeeth ;
Choi, Bokyung ;
Reicher, Joshua J. ;
Peng, Lily ;
Tse, Daniel ;
Etemadi, Mozziyar ;
Ye, Wenxing ;
Corrado, Greg ;
Naidich, David P. ;
Shetty, Shravya .
NATURE MEDICINE, 2019, 25 (06) :954-+
[7]   An artificial intelligence-enabled ECG algorithm for the identification of patients with atrial fibrillation during sinus rhythm: a retrospective analysis of outcome prediction [J].
Attia, Zachi, I ;
Noseworthy, Peter A. ;
Lopez-Jimenez, Francisco ;
Asirvatham, Samuel J. ;
Deshmukh, Abhishek J. ;
Gersh, Bernard J. ;
Carter, Rickey E. ;
Yao, Xiaoxi ;
Rabinstein, Alejandro A. ;
Erickson, Brad J. ;
Kapa, Suraj ;
Friedman, Paul A. .
LANCET, 2019, 394 (10201) :861-867
[8]   Clinical Feature-Based Machine Learning Model for 1-Year Mortality Risk Prediction of ST-Segment Elevation Myocardial Infarction in Patients with Hyperuricemia: A Retrospective Study [J].
Bai, Zhixun ;
Lu, Jing ;
Li, Ting ;
Ma, Yi ;
Liu, Zhijiang ;
Zhao, Ranzun ;
Wang, Zhenglong ;
Shi, Bei .
COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE, 2021, 2021 (2021)
[9]  
Bajwa Junaid, 2021, Future Healthc J, V8, pe188, DOI 10.7861/fhj.2021-0095
[10]   Genetics of Sudden Cardiac Death [J].
Bezzina, Connie R. ;
Lahrouchi, Najim ;
Priori, Silvia G. .
CIRCULATION RESEARCH, 2015, 116 (12) :1919-1936
123456789