Interpretation of the 2025 American Heart Association scientific statement: Artificial intelligence to enhance precision medicine in cardio-oncology_Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

Authors：

ZHANG Ying ^1,2 , LIAO Xiaoyang ¹ , YANG Hanfei ³ , CHEN Xi ⁴ , HUANG Chuanying ⁵ , LI Dongze ⁵ , JIA YU ¹ , SHEN Can ¹ , LEI Yi ¹ ,  YANG Rong ¹

1. General Practice Ward/International Medical Center Ward/Teaching & Research Section/General Practice Research Institute, General Practice Medical Center, West China Hospital, Sichuan University, Chengdu, 610041, P. R. China;
2. West China School of Nursing, West China Hospital, Sichuan University, Sichuan Province, Chengdu, 610041, P. R. China;
3. National Engineering Research Center for Cryptography Technology, Chengdu, 610041, P. R. China;
4. Abdominal Oncology Department, West China Hospital, Sichuan University, Chengdu, 610041, P. R. China;
5. Department of Emergency, West China Hospital, Sichuan University, Chengdu, 610041, P. R. China;

Corresponding author：

YANG Rong, Email: 976925601@qq.com

Keywords：

Artificial intelligence; cardio-oncology; cardiovascular risk; precision medicine

DOI：

10.7507/1007-4848.202505057

Video：

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[Abstract] Cardiovascular disease and cancer are the two leading chronic conditions contributing to global mortality. With the rising incidence of cancer, the prevalence of cancer therapy-related cardiovascular complications has also increased, driving the development of the emerging field of cardio-oncology. The advancement of precision medicine offers new opportunities for the individualized and targeted management of cardiovascular toxicities associated with cancer treatment. Artificial intelligence (AI) has the potential to overcome traditional limitations in medical data integration, dynamic monitoring, and interdisciplinary collaboration, thereby accelerating the application of precision medicine in cardio-oncology. By enabling personalized treatment and reducing cardiovascular complications in cancer patients, AI serves as a critical tool in this domain. This article provides an in-depth interpretation of the “Artificial intelligence to enhance precision medicine in cardio-oncology: a scientific statement from the American Heart Association” aiming to inform the integration of AI into precision medicine in China. The goal is to promote its application in the management of cardiovascular diseases related to cancer therapy and to achieve precision management in this context.

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2.	Addison D, Branch M, Baik AH, et al. Equity in cardio-oncology care and research: a scientific statement from the American Heart Association. Circulation, 2023, 148(3): 297-308.
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12.	Sawaya H, Sebag IA, Plana JC, et al. Assessment of echocardiography and biomarkers for the extended prediction of cardiotoxicity in patients treated with anthracyclines, taxanes, and trastuzumab. Circ Cardiovasc Imaging, 2012, 5(5): 596-603.
13.	Murtagh G, Januzzi JL, Scherrer-Crosbie M, et al. Circulating cardiovascular biomarkers in cancer therapeutics-related cardiotoxicity: review of critical challenges, solutions, and future directions. J Am Heart Assoc, 2023, 12(21): e029574.
14.	Cardinale D, Ciceri F, Latini R, et al. Anthracycline-induced cardiotoxicity: a multicenter randomised trial comparing two strategies for guiding prevention with enalapril: The International CardioOncology Society-one trial. Eur J Cancer, 2018, 94: 126-137.
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16.	Asnani A, Shi X, Farrell L, et al. Changes in citric acid cycle and nucleoside metabolism are associated with anthracycline cardiotoxicity in patients with breast cancer. J Cardiovasc Transl Res, 2020, 13(3): 349-356.
17.	Oikonomou EK, Khera R. Machine learning in precision diabetes care and cardiovascular risk prediction. Cardiovasc Diabetol, 2023, 22(1): 259.
18.	Heilbroner SP, Few R, Mueller J, et al. Predicting cardiac adverse events in patients receiving immune checkpoint inhibitors: a machine learning approach. J Immunother Cancer, 2021, 9(10): e003459.
19.	Chen H, Ouyang D, Baykaner T, et al. Artificial intelligence applications in cardio-oncology: leveraging high dimensional cardiovascular data. Front Cardiovasc Med, 2022, 9: 941148.
20.	Martinez DS, Noseworthy PA, Akbilgic O, et al. Artificial intelligence opportunities in cardio-oncology: overview with spotlight on electrocardiography. Am Heart J Plus, 2022, 15: 100143.
21.	Chao H, Shan H, Homayounieh F, et al. Deep learning predicts cardiovascular disease risks from lung cancer screening low dose computed tomography. Nat Commun, 2021, 12(1): 2963.
22.	Chaix MA, Parmar N, Kinnear C, et al. Machine learning identifies clinical and genetic factors associated with anthracycline cardiotoxicity in pediatric cancer survivors. JACC CardioOncol, 2020, 2(5): 690-706.
23.	Abud I, Zioti S, Moraes R. European Society of Cardiology: Highlights do ESC 2022. Arq Bras Cardiol, 2023, 120(5): e20230269.
24.	He B, Kwan AC, Cho JH, et al. Blinded, randomized trial of sonographer versus AI cardiac function assessment. Nature, 2023, 616(7957): 520-524.
25.	Oikonomou EK, Holste G, Yuan N, et al. A multimodal video-based AI biomarker for aortic stenosis development and progression. JAMA Cardiol, 2024, 9(6): 534-544.
26.	Chinn E, Arora R, Arnaout R, et al. ENRICHing medical imaging training sets enables more efficient machine learning. J Am Med Inform Assoc, 2023, 30(6): 1079-1090.
27.	Holste G, Oikonomou EK, Mortazavi BJ, et al. Efficient deep learning-based automated diagnosis from echocardiography with contrastive self-supervised learning. Commun Med (Lond), 2024, 4(1): 133.
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29.	Nadkarni PM, Ohno-Machado L, Chapman WW. Natural language processing: an introduction. J Am Med Inform Assoc, 2011, 18(5): 544-551.
30.	Sangha V, Nargesi AA, Dhingra LS, et al. Detection of left ventricular systolic dysfunction from electrocardiographic images. Circulation, 2023, 148(9): 765-777.
31.	Ashburner JM, Chang Y, Wang X, et al. Natural language processing to improve prediction of incident atrial fibrillation using electronic health records. J Am Heart Assoc, 2022, 11(15): e026014.
32.	Vaid A, Johnson KW, Badgeley MA, et al. Using deep-learning algorithms to simultaneously identify right and left ventricular dysfunction from the electrocardiogram. JACC Cardiovasc Imaging, 2022, 15(3): 395-410.
33.	Sarraju A, Ouyang D, Itchhaporia D. The opportunities and challenges of large language models in cardiology. JACC Adv, 2023, 2(7): 100438.
34.	Boonstra MJ, Weissenbacher D, Moore JH, et al. Artificial intelligence: revolutionizing cardiology with large language models. Eur Heart J, 2024, 45(5): 332-345.
35.	Li P, Zhang X, Zhu E, et al. Potential multidisciplinary use of large language models for addressing queries in cardio-oncology. J Am Heart Assoc, 2024, 13(6): e033584.
36.	Bakkar N, Kovalik T, Lorenzini I, et al. Artificial intelligence in neurodegenerative disease research: use of IBM Watson to identify additional RNA-binding proteins altered in amyotrophic lateral sclerosis. Acta Neuropathol, 2018, 135(2): 227-247.
37.	Service RF. AI conjures up potential new antibody drugs in a matter of months. Science, 2025, 367(6478): 348-351.
38.	周伊恒, 杨梓钰, 吕垚, 等. 美国心脏协会指南解读系列——《人工智能在心血管疾病中的应用科学声明》解读. 中国全科医学, 2024, 27(35): 4353-4357.Zhou YH, Yang ZY, Lv Y, et al. Interpretation of the use of artificial intelligence in improving outcomes in heart disease: a scientific statement from the American Heart Association. Chin Gen Pract, 2024, 27(35): 4353-4357.
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40.	Power JR, Alexandre J, Choudhary A, et al. Association of early electrical changes with cardiovascular outcomes in immune checkpoint inhibitor myocarditis. Arch Cardiovasc Dis, 2022, 115(5): 315-330.
41.	Christopoulos G, Attia ZI, Achenbach SJ, et al. Artificial intelligence electrocardiography to predict atrial fibrillation in patients with chronic lymphocytic leukemia. JACC CardioOncol, 2024, 6(2): 251-263.
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43.	Gabrielson KL, Mok GS, Nimmagadda S, et al. Detection of dose response in chronic doxorubicin-mediated cell death with cardiac technetium 99m annexin V single-photon emission computed tomography. Mol Imaging, 2008, 7(3): 132-138.
44.	Dolgin E. Using DNA, radiation therapy gets personal. Science, 2016, 353(6306): 1348-1349.
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47.	International Organization for Standardization. Health informatics: electronic health record: definition, scope and context. ISO/TR 20514: 2005.
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1. Spannbauer A, Bergler-Klein J. Cardio-oncology: a new discipline in medicine and its relevance to hematology. Hamostaseologie, 2024, 44(4): 255-267.
2. Addison D, Branch M, Baik AH, et al. Equity in cardio-oncology care and research: a scientific statement from the American Heart Association. Circulation, 2023, 148(3): 297-308.
3. Beavers CJ, Rodgers JE, Bagnola AJ, et al. Cardio-oncology drug interactions: a scientific statement from the American Heart Association. Circulation, 2022, 145(15): e811-e838.
4. 于坚, 王江涛, 刘新亚, 等. 免疫检查点抑制剂相关心血管不良反应流行病学、发病机制及诊断与治疗进展. 实用心脑肺血管病杂志, 2025, 33(2): 130-135.Yu J, Wang JT, Liu XY, et al. Progress on epidemiology, pathogenesis, diagnosis and treatment of immune checkpoint inhibitors-related cardiovascular adverse events. Pract J Card Cereb Pneum Vasc Dis, 2025, 33(2): 130-135.
5. Lyon AR, López-Fernández T, Couch LS, et al. 2022 ESC guidelines on cardio-oncology developed in collaboration with the European Hematology Association (EHA), the European Society for Therapeutic Radiology and Oncology (ESTRO) and the International Cardio-Oncology Society (IC-OS). Eur Heart J, 2022, 43(41): 4229-4361.
6. 李伟, 杨若南, 徐萍, 等. 2023年肿瘤精准医学发展态势. 生命科学, 2024, 36(1): 63-71.Li W, Yang RN, Xu P, et al. Development trend of precision oncology in 2023. Chin Bull Life Sci, 2024, 36(1): 63-71.
7. 王玉珏, 聂萌, 徐依朋, 等. 肿瘤早筛早诊与监测标志物. 中国科学基金, 2025, 39(1): 80-90.Wang YJ, Nei M, Xu YP, et al. Biomarkers for early cancer screening, diagnosis, and dynamic monitoring. Bull Natl Sci Found China, 2025, 39(1): 80-90.
8. Khera R, Asnani AH, Krive J, 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(2): e000097.
9. Moslehi JJ. Cardiovascular toxic effects of targeted cancer therapies. N Engl J Med, 2016, 375(15): 1457-1467.
10. 孟真, 任景怡. 《欧洲心脏病学会肿瘤心脏病学指南2022版》要点解读. 中国心血管杂志, 2022, 27(6): 507-512.Meng Z, Ren JY. Interpretation of 2022 European Society of Cardiology guidelines on cardio-oncology. Chin J Cardiovasc Med, 2022, 27(6): 507-512.
11. Addison D, Neilan TG, Barac A, et al. Cardiovascular imaging in contemporary cardio-oncology: a scientific statement from the American Heart Association. Circulation, 2023, 148(16): 1271-1286.
12. Sawaya H, Sebag IA, Plana JC, et al. Assessment of echocardiography and biomarkers for the extended prediction of cardiotoxicity in patients treated with anthracyclines, taxanes, and trastuzumab. Circ Cardiovasc Imaging, 2012, 5(5): 596-603.
13. Murtagh G, Januzzi JL, Scherrer-Crosbie M, et al. Circulating cardiovascular biomarkers in cancer therapeutics-related cardiotoxicity: review of critical challenges, solutions, and future directions. J Am Heart Assoc, 2023, 12(21): e029574.
14. Cardinale D, Ciceri F, Latini R, et al. Anthracycline-induced cardiotoxicity: a multicenter randomised trial comparing two strategies for guiding prevention with enalapril: The International CardioOncology Society-one trial. Eur J Cancer, 2018, 94: 126-137.
15. Henriksen PA, Hall P, Macpherson IR, et al. Multicenter, prospective, randomized controlled trial of high-sensitivity cardiac troponin I-guided combination angiotensin receptor blockade and beta-blocker therapy to prevent anthracycline cardiotoxicity: The Cardiac CARE Trial. Circulation, 2023, 148(21): 1680-1690.
16. Asnani A, Shi X, Farrell L, et al. Changes in citric acid cycle and nucleoside metabolism are associated with anthracycline cardiotoxicity in patients with breast cancer. J Cardiovasc Transl Res, 2020, 13(3): 349-356.
17. Oikonomou EK, Khera R. Machine learning in precision diabetes care and cardiovascular risk prediction. Cardiovasc Diabetol, 2023, 22(1): 259.
18. Heilbroner SP, Few R, Mueller J, et al. Predicting cardiac adverse events in patients receiving immune checkpoint inhibitors: a machine learning approach. J Immunother Cancer, 2021, 9(10): e003459.
19. Chen H, Ouyang D, Baykaner T, et al. Artificial intelligence applications in cardio-oncology: leveraging high dimensional cardiovascular data. Front Cardiovasc Med, 2022, 9: 941148.
20. Martinez DS, Noseworthy PA, Akbilgic O, et al. Artificial intelligence opportunities in cardio-oncology: overview with spotlight on electrocardiography. Am Heart J Plus, 2022, 15: 100143.
21. Chao H, Shan H, Homayounieh F, et al. Deep learning predicts cardiovascular disease risks from lung cancer screening low dose computed tomography. Nat Commun, 2021, 12(1): 2963.
22. Chaix MA, Parmar N, Kinnear C, et al. Machine learning identifies clinical and genetic factors associated with anthracycline cardiotoxicity in pediatric cancer survivors. JACC CardioOncol, 2020, 2(5): 690-706.
23. Abud I, Zioti S, Moraes R. European Society of Cardiology: Highlights do ESC 2022. Arq Bras Cardiol, 2023, 120(5): e20230269.
24. He B, Kwan AC, Cho JH, et al. Blinded, randomized trial of sonographer versus AI cardiac function assessment. Nature, 2023, 616(7957): 520-524.
25. Oikonomou EK, Holste G, Yuan N, et al. A multimodal video-based AI biomarker for aortic stenosis development and progression. JAMA Cardiol, 2024, 9(6): 534-544.
26. Chinn E, Arora R, Arnaout R, et al. ENRICHing medical imaging training sets enables more efficient machine learning. J Am Med Inform Assoc, 2023, 30(6): 1079-1090.
27. Holste G, Oikonomou EK, Mortazavi BJ, et al. Efficient deep learning-based automated diagnosis from echocardiography with contrastive self-supervised learning. Commun Med (Lond), 2024, 4(1): 133.
28. Christensen M, Vukadinovic M, Yuan N, et al. Vision-language foundation model for echocardiogram interpretation. Nat Med, 2024, 30(5): 1481-1488.
29. Nadkarni PM, Ohno-Machado L, Chapman WW. Natural language processing: an introduction. J Am Med Inform Assoc, 2011, 18(5): 544-551.
30. Sangha V, Nargesi AA, Dhingra LS, et al. Detection of left ventricular systolic dysfunction from electrocardiographic images. Circulation, 2023, 148(9): 765-777.
31. Ashburner JM, Chang Y, Wang X, et al. Natural language processing to improve prediction of incident atrial fibrillation using electronic health records. J Am Heart Assoc, 2022, 11(15): e026014.
32. Vaid A, Johnson KW, Badgeley MA, et al. Using deep-learning algorithms to simultaneously identify right and left ventricular dysfunction from the electrocardiogram. JACC Cardiovasc Imaging, 2022, 15(3): 395-410.
33. Sarraju A, Ouyang D, Itchhaporia D. The opportunities and challenges of large language models in cardiology. JACC Adv, 2023, 2(7): 100438.
34. Boonstra MJ, Weissenbacher D, Moore JH, et al. Artificial intelligence: revolutionizing cardiology with large language models. Eur Heart J, 2024, 45(5): 332-345.
35. Li P, Zhang X, Zhu E, et al. Potential multidisciplinary use of large language models for addressing queries in cardio-oncology. J Am Heart Assoc, 2024, 13(6): e033584.
36. Bakkar N, Kovalik T, Lorenzini I, et al. Artificial intelligence in neurodegenerative disease research: use of IBM Watson to identify additional RNA-binding proteins altered in amyotrophic lateral sclerosis. Acta Neuropathol, 2018, 135(2): 227-247.
37. Service RF. AI conjures up potential new antibody drugs in a matter of months. Science, 2025, 367(6478): 348-351.
38. 周伊恒, 杨梓钰, 吕垚, 等. 美国心脏协会指南解读系列——《人工智能在心血管疾病中的应用科学声明》解读. 中国全科医学, 2024, 27(35): 4353-4357.Zhou YH, Yang ZY, Lv Y, et al. Interpretation of the use of artificial intelligence in improving outcomes in heart disease: a scientific statement from the American Heart Association. Chin Gen Pract, 2024, 27(35): 4353-4357.
39. Elias P, Jain SS, Poterucha T, et al. Artificial intelligence for cardiovascular care—Part 1: Advances: JACC review topic of the week. J Am Coll Cardiol, 2024, 83(24): 2472-2486.
40. Power JR, Alexandre J, Choudhary A, et al. Association of early electrical changes with cardiovascular outcomes in immune checkpoint inhibitor myocarditis. Arch Cardiovasc Dis, 2022, 115(5): 315-330.
41. Christopoulos G, Attia ZI, Achenbach SJ, et al. Artificial intelligence electrocardiography to predict atrial fibrillation in patients with chronic lymphocytic leukemia. JACC CardioOncol, 2024, 6(2): 251-263.
42. Prifti E, Fall A, Davogustto G, et al. Deep learning analysis of electrocardiogram for risk prediction of drug-induced arrhythmias and diagnosis of long QT syndrome. Eur Heart J, 2021, 42(38): 3948-3961.
43. Gabrielson KL, Mok GS, Nimmagadda S, et al. Detection of dose response in chronic doxorubicin-mediated cell death with cardiac technetium 99m annexin V single-photon emission computed tomography. Mol Imaging, 2008, 7(3): 132-138.
44. Dolgin E. Using DNA, radiation therapy gets personal. Science, 2016, 353(6306): 1348-1349.
45. Rhee JW, Ky B, Armenian SH, et al. Primer on biomarker discovery in cardio-oncology: application of omics technologies. JACC CardioOncol, 2020, 2(3): 379-384.
46. Wilcox NS, Rotz SJ, Mullen M, et al. Sex-specific cardiovascular risks of cancer and its therapies. Circ Res, 2022, 130(4): 632-651.
47. International Organization for Standardization. Health informatics: electronic health record: definition, scope and context. ISO/TR 20514: 2005.
48. Kamphuis JAM, Linschoten M, Cramer MJ, et al. ONCOR: design of the Dutch cardio-oncology registry. Neth Heart J, 2021, 29(5): 288-294.
49. Al-Droubi SS, Jahangir E, Kochendorfer KM, et al. Artificial intelligence modelling to assess the risk of cardiovascular disease in oncology patients. Eur Heart J Digit Health, 2023, 4(4): 302-315.
50. 中华人民共和国中央人民政府. 关于印发健康中国行动—癌症防治行动实施方案(2023—2030年)的通知. 国卫疾控发〔2023〕1号.National Health Commission of the People's Republic of China. Notice on issuing the healthy China initiative: cancer prevention and treatment implementation plan (2023-2030). Document No. 2023-1.
51. 国家医院管理研究信息系统. 中国乳腺癌标准数据库. CBSCD v2.0. 2020.National Hospital Administration Research Information System. China Breast Cancer Standardized Database. CBSCD v2.0. 2020.
52. 中国肿瘤防治数据库. 中国肿瘤防治数据库. CCOPD v4.0. 2004.China Cancer Prevention and Treatment Database. China Cancer Prevention and Treatment Database. CCOPD v4.0. 2004.
53. 中国抗癌协会肿瘤营养专业委员会. 中国肿瘤营养数据库. CNCD v3.2. 2012.Professional Committee of Cancer Nutrition, China Anti-Cancer Association. China Cancer Nutrition Database. CNCD v3.2. 2012.

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Latest ArticlesInterpretation of the 2025 American Heart Association scientific statement: Artificial intelligence to enhance precision medicine in cardio-oncology

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