Interpretation of the "Artificial intelligence to enhance precision medicine in cardio-oncology: A scientific statement from the American Heart Association"_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, 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: yr1567@wchscu.cn

Keywords：

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

DOI：

10.7507/1007-4848.202505057

Video：

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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.

Citation： ZHANG Ying, LIAO Xiaoyang, YANG Hanfei, CHEN Xi, HUANG Chuanying, LI Dongze, JIA Yu, SHEN Can, LEI Yi, YANG Rong. Interpretation of the "Artificial intelligence to enhance precision medicine in cardio-oncology: A scientific statement from the American Heart Association". Chinese Journal of Clinical Thoracic and Cardiovascular Surgery, 2025, 32(10): 1360-1367. doi: 10.7507/1007-4848.202505057 Copy

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.	于堅, 王江濤, 劉新亞, 等. 免疫檢查點抑制劑相關心血管不良反應流行病學、發病機制及診斷與治療進展. 實用心腦肺血管病雜志, 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.	李偉, 楊若南, 徐萍, 等. 2023年腫瘤精準醫學發展態勢. 生命科學, 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.	王玉玨, 聶萌, 徐依朋, 等. 腫瘤早篩早診與監測標志物. 中國科學基金, 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. 《歐洲心臟病學會腫瘤心臟病學指南2022版》要點解讀. 中國心血管雜志, 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.	Demissei BG, Hubbard RA, Zhang L, et al. Changes in cardiovascular biomarkers with breast cancer therapy and associations with cardiac dysfunction. J Am Heart Assoc, 2020, 9: e014708.
17.	中國醫師協會檢驗醫師分會心血管專家委員會, 中華醫學會心血管病學分會腫瘤心臟病學組. 心臟生物標志物用于腫瘤治療相關心血管毒性的篩查和管理中國專家共識(2024版). 中華醫學雜志, 2024, 104(36): 3371-3385.Cardiovascular Expert Committee of Chinese Medical Doctor Association of Laboratory Medicine, Section of Cardio-Oncology Group of Chinese Society of Cardiology. Chinese expert consensus on cardiac biomarkers for monitoring and management of cardiovascular toxicity in cancer therapy (2024 edition). Natl Med J China, 2024, 104(36): 3371-3385.
18.	Oikonomou EK, Khera R. Machine learning in precision diabetes care and cardiovascular risk prediction. Cardiovasc Diabetol, 2023, 22(1): 259.
19.	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.
20.	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.
21.	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.
22.	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.
23.	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.
24.	Abud I, Zioti S, Moraes R. European Society of Cardiology: highlights do ESC 2022. Arq Bras Cardiol, 2023, 120(5): e20230269.
25.	He B, Kwan AC, Cho JH, et al. Blinded, randomized trial of sonographer versus AI cardiac function assessment. Nature, 2023, 616(7957): 520-524.
26.	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.
27.	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.
28.	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.
29.	Christensen M, Vukadinovic M, Yuan N, et al. Vision-language foundation model for echocardiogram interpretation. Nat Med, 2024, 30(5): 1481-1488.
30.	Nadkarni PM, Ohno-Machado L, Chapman WW. Natural language processing: an introduction. J Am Med Inform Assoc, 2011, 18(5): 544-551.
31.	Sangha V, Nargesi AA, Dhingra LS, et al. Detection of left ventricular systolic dysfunction from electrocardiographic images. Circulation, 2023, 148(9): 765-777.
32.	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.
33.	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.
34.	Sarraju A, Ouyang D, Itchhaporia D. The opportunities and challenges of large language models in cardiology. JACC Adv, 2023, 2(7): 100438.
35.	Boonstra MJ, Weissenbacher D, Moore JH, et al. Artificial intelligence: revolutionizing cardiology with large language models. Eur Heart J, 2024, 45(5): 332-345.
36.	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.
37.	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.
38.	Service RF. AI-designed antibody candidates hit a crucial target. Science, 2025, 388(6748): 689-690.
39.	周伊恒, 楊梓鈺, 呂垚, 等. 美國心臟協會指南解讀系列—《人工智能在心血管疾病中的應用科學聲明》解讀. 中國全科醫學, 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.
40.	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.
41.	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.
42.	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.
43.	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.
44.	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.
45.	Dolgin E. Using DNA, radiation therapy gets personal. Science, 2016, 353(6306): 1348-1349.
46.	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.
47.	Wilcox NS, Rotz SJ, Mullen M, et al. Sex-specific cardiovascular risks of cancer and its therapies. Circ Res, 2022, 130(4): 632-651.
48.	International Organization for Standardization. Health informatics: electronic health record: definition, scope and context. ISO/TR 20514: 2005.
49.	Kamphuis JAM, Linschoten M, Cramer MJ, et al. ONCOR: design of the Dutch cardio-oncology registry. Neth Heart J, 2021, 29(5): 288-294.
50.	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.
51.	國家衛生健康委,?國家發展改革委, 教育部, 等. 關于印發健康中國行動—癌癥防治行動實施方案(2023—2030年)的通知. 國衛疾控發〔2023〕1號.National Health Commission of the People's Republic of China, National Development and Reform Commission, Ministry of Education of the People's Republic of China, et al. Notice on issuing the healthy China initiative: cancer prevention and treatment implementation plan (2023-2030). Document No. 2023-1.

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. 于堅, 王江濤, 劉新亞, 等. 免疫檢查點抑制劑相關心血管不良反應流行病學、發病機制及診斷與治療進展. 實用心腦肺血管病雜志, 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. 李偉, 楊若南, 徐萍, 等. 2023年腫瘤精準醫學發展態勢. 生命科學, 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. 王玉玨, 聶萌, 徐依朋, 等. 腫瘤早篩早診與監測標志物. 中國科學基金, 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. 《歐洲心臟病學會腫瘤心臟病學指南2022版》要點解讀. 中國心血管雜志, 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. Demissei BG, Hubbard RA, Zhang L, et al. Changes in cardiovascular biomarkers with breast cancer therapy and associations with cardiac dysfunction. J Am Heart Assoc, 2020, 9: e014708.
17. 中國醫師協會檢驗醫師分會心血管專家委員會, 中華醫學會心血管病學分會腫瘤心臟病學組. 心臟生物標志物用于腫瘤治療相關心血管毒性的篩查和管理中國專家共識(2024版). 中華醫學雜志, 2024, 104(36): 3371-3385.Cardiovascular Expert Committee of Chinese Medical Doctor Association of Laboratory Medicine, Section of Cardio-Oncology Group of Chinese Society of Cardiology. Chinese expert consensus on cardiac biomarkers for monitoring and management of cardiovascular toxicity in cancer therapy (2024 edition). Natl Med J China, 2024, 104(36): 3371-3385.
18. Oikonomou EK, Khera R. Machine learning in precision diabetes care and cardiovascular risk prediction. Cardiovasc Diabetol, 2023, 22(1): 259.
19. 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.
20. 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.
21. 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.
22. 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.
23. 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.
24. Abud I, Zioti S, Moraes R. European Society of Cardiology: highlights do ESC 2022. Arq Bras Cardiol, 2023, 120(5): e20230269.
25. He B, Kwan AC, Cho JH, et al. Blinded, randomized trial of sonographer versus AI cardiac function assessment. Nature, 2023, 616(7957): 520-524.
26. 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.
27. 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.
28. 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.
29. Christensen M, Vukadinovic M, Yuan N, et al. Vision-language foundation model for echocardiogram interpretation. Nat Med, 2024, 30(5): 1481-1488.
30. Nadkarni PM, Ohno-Machado L, Chapman WW. Natural language processing: an introduction. J Am Med Inform Assoc, 2011, 18(5): 544-551.
31. Sangha V, Nargesi AA, Dhingra LS, et al. Detection of left ventricular systolic dysfunction from electrocardiographic images. Circulation, 2023, 148(9): 765-777.
32. 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.
33. 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.
34. Sarraju A, Ouyang D, Itchhaporia D. The opportunities and challenges of large language models in cardiology. JACC Adv, 2023, 2(7): 100438.
35. Boonstra MJ, Weissenbacher D, Moore JH, et al. Artificial intelligence: revolutionizing cardiology with large language models. Eur Heart J, 2024, 45(5): 332-345.
36. 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.
37. 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.
38. Service RF. AI-designed antibody candidates hit a crucial target. Science, 2025, 388(6748): 689-690.
39. 周伊恒, 楊梓鈺, 呂垚, 等. 美國心臟協會指南解讀系列—《人工智能在心血管疾病中的應用科學聲明》解讀. 中國全科醫學, 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.
40. 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.
41. 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.
42. 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.
43. 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.
44. 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.
45. Dolgin E. Using DNA, radiation therapy gets personal. Science, 2016, 353(6306): 1348-1349.
46. 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.
47. Wilcox NS, Rotz SJ, Mullen M, et al. Sex-specific cardiovascular risks of cancer and its therapies. Circ Res, 2022, 130(4): 632-651.
48. International Organization for Standardization. Health informatics: electronic health record: definition, scope and context. ISO/TR 20514: 2005.
49. Kamphuis JAM, Linschoten M, Cramer MJ, et al. ONCOR: design of the Dutch cardio-oncology registry. Neth Heart J, 2021, 29(5): 288-294.
50. 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.
51. 國家衛生健康委,?國家發展改革委, 教育部, 等. 關于印發健康中國行動—癌癥防治行動實施方案(2023—2030年)的通知. 國衛疾控發〔2023〕1號.National Health Commission of the People's Republic of China, National Development and Reform Commission, Ministry of Education of the People's Republic of China, et al. Notice on issuing the healthy China initiative: cancer prevention and treatment implementation plan (2023-2030). Document No. 2023-1.

Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

Interpretation of the "Artificial intelligence to enhance precision medicine in cardio-oncology: A scientific statement from the American Heart Association"

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