Poalelungi DG, Musat CL, Fulga A, Neagu M, Neagu AI, Piraianu AI, et al. Advancing patient care: how artificial intelligence is transforming healthcare. J Pers Med. 2023. https://doi.org/10.3390/jpm13081214.
Article PubMed PubMed Central Google Scholar
Alowais SA, Alghamdi SS, Alsuhebany N, Alqahtani T, Alshaya AI, Almohareb SN, et al. Revolutionizing healthcare: the role of artificial intelligence in clinical practice. BMC Med Educ. 2023;23:689. https://doi.org/10.1186/s12909-023-04698-z.
Article PubMed PubMed Central Google Scholar
Lin A, Kolossváry M, Motwani M, Išgum I, Maurovich-Horvat P, Slomka PJ, et al. Artificial intelligence in cardiovascular imaging for risk stratification in coronary artery disease. Radiology: Cardiothoracic Imaging. 2021;3:e200512. https://doi.org/10.1148/ryct.2021200512.
Article PubMed PubMed Central Google Scholar
Khalifa M, Albadawy M. Artificial intelligence for clinical prediction: exploring key domains and essential functions. Computer Methods and Programs in Biomedicine Update. 2024;5:100148. https://doi.org/10.1016/j.cmpbup.2024.100148.
Narula S, Shameer K, Salem Omar AM, Dudley JT, Sengupta PP. Machine-learning algorithms to automate morphological and functional assessments in 2D echocardiography. J Am Coll Cardiol. 2016;68:2287–95. https://doi.org/10.1016/j.jacc.2016.08.062.
Playford D, Bordin E, Mohamad R, Stewart S, Strange G. Enhanced diagnosis of severe aortic stenosis using artificial intelligence: a proof-of-concept study of 530,871 echocardiograms. JACC Cardiovasc Imaging. 2020;13:1087–90. https://doi.org/10.1016/j.jcmg.2019.10.013.
Huhulea EN, Huang L, Eng S, Sumawi B, Huang A, Aifuwa E, et al. Artificial intelligence advancements in oncology: a review of current trends and future directions. Biomedicines. 2025. https://doi.org/10.3390/biomedicines13040951.
Article PubMed PubMed Central Google Scholar
Khera R, Asnani AH, Krive J, Addison D, Zhu H, Vasbinder A, et al. Artificial intelligence to enhance precision medicine in cardio-oncology: a scientific statement from the American Heart Association. Circ Genom Precis Med. 2025;18:e000097. https://doi.org/10.1161/hcg.0000000000000097.
Article PubMed PubMed Central Google Scholar
Visseren FLJ, Mach F, Smulders YM, Carballo D, Koskinas KC, Bäck M, Benetos A, Biffi A, Boavida JM, Capodanno D. 2021 ESC guidelines on cardiovascular disease prevention in clinical practice. Eur Heart J. 2021;42:3227–337. https://doi.org/10.1093/eurheartj/ehab484.
Hippisley-Cox J, Coupland C, Brindle P. DevelopmentandvalidationofQRISK3riskpredictionalgorithmstoestimatefutureriskofcardiovasculardisease:prospectivecohortstudy. Bmj. 2017;357:j2099. https://doi.org/10.1136/bmj.j2099.
Article PubMed PubMed Central Google Scholar
McCracken C, Condurache DG, Szabo L, Elghazaly H, Walter FM, Mead AJ, et al. Predictive performance of cardiovascular risk scores in cancer survivors from the UK biobank. JACC: CardioOncology. 2024;6:575–88. https://doi.org/10.1016/j.jaccao.2024.05.015.
Article PubMed PubMed Central Google Scholar
Soh CH, Marwick TH. Comparison of heart failure risk assessment tools among cancer survivors. Cardio-Oncol. 2024;10:67. https://doi.org/10.1186/s40959-024-00267-5.
Khan SS, Matsushita K, Sang Y, Ballew SH, Grams ME, Surapaneni A, et al. Development and validation of the American Heart Association’s PREVENT equations. Circulation. 2024;149:430–49. https://doi.org/10.1161/circulationaha.123.067626.
Lyon AR, Dent S, Stanway S, Earl H, Brezden-Masley C, Cohen-Solal A, et al. Baseline cardiovascular risk assessment in cancer patients scheduled to receive cardiotoxic cancer therapies: a position statement and new risk assessment tools from the Cardio-Oncology study group of the heart failure association of the European society of cardiology in collaboration with the international Cardio-Oncology society. Eur J Heart Fail. 2020;22:1945–60. https://doi.org/10.1002/ejhf.1920.
Liu T, Krentz A, Lu L, Curcin V. Machine learning based prediction models for cardiovascular disease risk using electronic health records data: systematic review and meta-analysis. Eur Heart J. 2025;6:7–22. https://doi.org/10.1093/ehjdh/ztae080.
Al-Droubi SS, Jahangir E, Kochendorfer KM, Krive M, Laufer-Perl M, Gilon D, et al. Artificial intelligence modelling to assess the risk of cardiovascular disease in oncology patients. Eur Heart J. 2023;4:302–15. https://doi.org/10.1093/ehjdh/ztad031.
Biton S, Gendelman S, Ribeiro AH, Miana G, Moreira C, Ribeiro ALP, et al. Atrial fibrillation risk prediction from the 12-lead electrocardiogram using digital biomarkers and deep representation learning. Eur Heart J Digit Health. 2021;2:576–85. https://doi.org/10.1093/ehjdh/ztab071.
Article PubMed PubMed Central Google Scholar
Christopoulos G, Attia ZI, Achenbach SJ, Rabe KG, Call TG, Ding W, et al. Artificial intelligence electrocardiography to predict atrial fibrillation in patients with chronic lymphocytic leukemia. JACC CardioOncol. 2024;6:251–63. https://doi.org/10.1016/j.jaccao.2024.02.006.
Article PubMed PubMed Central Google Scholar
Attia ZI, Kapa S, Lopez-Jimenez F, McKie PM, Ladewig DJ, Satam G, et al. Screening for cardiac contractile dysfunction using an artificial intelligence-enabled electrocardiogram. Nat Med. 2019;25:70–4. https://doi.org/10.1038/s41591-018-0240-2.
Article CAS PubMed Google Scholar
Madan N, Lucas J, Akhter N, Collier P, Cheng F, Guha A, et al. Artificial intelligence and imaging: opportunities in cardio-oncology. American Heart Journal Plus: Cardiology Research and Practice. 2022;15:100126. https://doi.org/10.1016/j.ahjo.2022.100126.
Article PubMed PubMed Central Google Scholar
Baldassarre LA, Ganatra S, Lopez-Mattei J, Yang EH, Zaha VG, Wong TC, Ayoub C, DeCara JM, Dent S, Deswal A. Advances in multimodality imaging in Cardio-Oncology: JACC State-of-the-Art review. J Am Coll Cardiol. 2022;80:1560–78. https://doi.org/10.1016/j.jacc.2022.08.743.
Ghorbani A, Ouyang D, Abid A, He B, Chen JH, Harrington RA, et al. Deep learning interpretation of echocardiograms. NPJ Digit Med. 2020;3:10. https://doi.org/10.1038/s41746-019-0216-8.
Article PubMed PubMed Central Google Scholar
Edwards LA, Yang C, Sharma S, Chen Z-H, Gorantla L, Joshi SA, Longhi NJ, Worku N, Yang JS, Di Martinez B. Building a machine learning-assisted echocardiography prediction tool for children at risk for cancer therapy-related cardiomyopathy. Cardio-Oncology. 2024;10:66. https://doi.org/10.1186/s40959-024-00268-4.
Article PubMed PubMed Central Google Scholar
Papadopoulou S-L, Dionysopoulos D, Mentesidou V, Loga K, Michalopoulou S, Koukoutzeli C, et al. Artificial intelligence-assisted evaluation of cardiac function by oncology staff in chemotherapy patients. Eur Heart J. 2024;5:278–87. https://doi.org/10.1093/ehjdh/ztae017.
Zhang J, Gajjala S, Agrawal P, Tison GH, Hallock LA, Beussink-Nelson L, et al. Fully automated echocardiogram interpretation in clinical practice. Circulation. 2018;138:1623–35. https://doi.org/10.1161/circulationaha.118.034338.
Article PubMed PubMed Central Google Scholar
Mushcab H, Al Ramis M, AlRujaib A, Eskandarani R, Sunbul T, AlOtaibi A, Obaidan M, Al Harbi R, Aljabri D. Application of artificial intelligence in Cardio-Oncology imaging for cancer Therapy-Related cardiovascular toxicity: systematic review. JMIR Cancer. 2025;11:e63964. https://doi.org/10.2196/63964.
Article PubMed PubMed Central Google Scholar
Shen H, Lian Y, Yin J, Zhu M, Yang C, Tu C, et al. Cardiovascular risk stratification by automatic coronary artery calcium scoring on pretreatment chest computed tomography in diffuse large B-cell lymphoma receiving Anthracycline-based chemotherapy: a multicenter study. Circulation: Cardiovascular Imaging. 2023;16:e014829. https://doi.org/10.1161/circimaging.122.014829.
Kar J, Cohen MV, McQuiston SA, Malozzi CM. Can global longitudinal strain (GLS) with magnetic resonance prognosticate early cancer therapy-related cardiac dysfunction (CTRCD) in breast cancer patients, a prospective study? Magn Reson Imaging. 2023;97:68–81. https://doi.org/10.1016/j.mri.2022.12.015.
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