Interpretation of “Use of artificial intelligence in improving outcomes in heart disease: A scientific statement from the American Heart Association” in 2024_Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

Authors：

CHEN Jinhua ¹ , ZHANG Heng ^1,2 ,  ZHENG Zhe ^1,2

1. State Key Laboratory of Cardiovascular Disease, National Clinical Research Center for Cardiovascular Diseases, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences, Beijing, 100037, P. R. China;
2. Department of Cardiovascular Surgery, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College Beijing, 100037, P. R. China;

Corresponding author：

ZHENG Zhe, Email: zhengzhe@fuwai.com

Keywords：

Artificial intelligence; electrocardiography; electronic health records; ethics; genetics; heart diseases; monitoring; interpretation

DOI：

10.7507/1007-4848.202504054

Video：

Export PDF Favorites Scan Get Citation

Abstract Full text Figures/Tables Video References Cited by

[Abstract] Currently, the academic community, industry, and governmental institutions worldwide are dedicated to developing and applying artificial intelligence and other advanced analytical tools to drive the transformation of healthcare services. However, there are still many challenges, with only a few artificial intelligence tools having achieved sufficient effectiveness in improving clinical outcomes for cardiovascular diseases and strokes to be widely used. In response, the American Heart Association has formulated related scientific statements outlining the latest research developments in artificial intelligence algorithms and data science for the diagnosis, classification, and treatment of cardiovascular diseases. These statements also summarize the current best practices, research gaps, and existing challenges of artificial intelligence tools, aiming to promote the development of this field. This article interprets this scientific statement in conjunction with the relevant research practices of the author's team.

1.	Wu JTY, de la Hoz MÁA, Kuo PC, et al. Developing and validating multi-modal models for mortality prediction in COVID-19 patients: A multi-center retrospective study. J Digit Imaging, 2022, 35(6): 1514-1529.
2.	Gehrmann S, Dernoncourt F, Li Y, et al. Comparing deep learning and concept extraction based methods for patient phenotyping from clinical narratives. PLoS One, 2018, 13(2): e0192360.
3.	Komorowski M, Celi LA, Badawi O, et al. The Artificial Intelligence Clinician learns optimal treatment strategies for sepsis in intensive care. Nat Med, 2018, 24(11): 1716-1720.
4.	Hu JR, Power JR, Zannad F, et al. Artificial intelligence and digital tools for design and execution of cardiovascular clinical trials. Eur Heart J, 2025, 46(9): 814-826.
5.	Armoundas AA, Narayan SM, Arnett DK, et al. Use of artificial intelligence in improving outcomes in heart disease: A scientific statement from the American Heart Association. Circulation, 2024, 149(14): e1028-e1050.
6.	Tatsugami F, Higaki T, Nakamura Y, et al. Deep learning-based image restoration algorithm for coronary CT angiography. Eur Radiol, 2019, 29(10): 5322-5329.
7.	Howard JP, Fisher L, Shun-Shin MJ, et al. Cardiac rhythm device identification using neural networks. JACC Clin Electrophysiol, 2019, 5(5): 576-586.
8.	Seetharam K, Brito D, Farjo PD, et al. The role of artificial intelligence in cardiovascular imaging: State of the art review. Front Cardiovasc Med, 2020, 7: 618849.
9.	van den Oever LB, Vonder M, van Assen M, et al. Application of artificial intelligence in cardiac CT: From basics to clinical practice. Eur J Radiol, 2020, 128: 108969.
10.	van Hamersvelt RW, Zreik M, Voskuil M, et al. Deep learning analysis of left ventricular myocardium in CT angiographic intermediate-degree coronary stenosis improves the diagnostic accuracy for identification of functionally significant stenosis. Eur Radiol, 2019, 29(5): 2350-2359.
11.	de Vos BD, Wolterink JM, Leiner T, et al. Direct automatic coronary calcium scoring in cardiac and chest CT. IEEE Trans Med Imaging, 2019, 38(9): 2127-2138.
12.	Ruijsink B, Puyol-Antón E, Oksuz I, et al. Fully automated, quality-controlled cardiac analysis from CMR: Validation and large-scale application to characterize cardiac function. JACC Cardiovasc Imaging, 2020, 13(3): 684-695.
13.	Narayan SM, Wang PJ, Daubert JP. New concepts in sudden cardiac arrest to address an intractable epidemic: JACC state-of-the-art review. J Am Coll Cardiol, 2019, 73(1): 70-88.
14.	Weikert T, Francone M, Abbara S, et al. Machine learning in cardiovascular radiology: ESCR position statement on design requirements, quality assessment, current applications, opportunities, and challenges. Eur Radiol, 2021, 31(6): 3909-3922.
15.	Harvey H, Glocker B. A standardised approach for preparing imaging data for machine learning tasks in radiology. In: Ranschaert ER, Morozov S, Algra PR, eds. Artificial Intelligence in Medical Imaging: Opportunities, Applications and Risks. Cham: Springer, 2019: 61-72.
16.	Wilkinson MD, Dumontier M, Aalbersberg IJJ, et al. The FAIR guiding principles for scientific data management and stewardship. Sci Data, 2016, 3: 160018.
17.	Al-Zaiti SS, Martin-Gill C, Zègre-Hemsey JK, et al. Machine learning for ECG diagnosis and risk stratification of occlusion myocardial infarction. Nat Med, 2023, 29(7): 1804-1813.
18.	Attia ZI, Noseworthy PA, Lopez-Jimenez F, et al. An artificial intelligence-enabled ECG algorithm for the identification of patients with atrial fibrillation during sinus rhythm: A retrospective analysis of outcome prediction. Lancet, 2019, 394(10201): 861-867.
19.	Harmon DM, Carter RE, Cohen-Shelly M, et al. Real-world performance, long-term efficacy, and absence of bias in the artificial intelligence enhanced electrocardiogram to detect left ventricular systolic dysfunction. Eur Heart J Digit Health, 2022, 3(2): 238-244.
20.	Bachtiger P, Petri CF, Scott FE, et al. Point-of-care screening for heart failure with reduced ejection fraction using artificial intelligence during ECG-enabled stethoscope examination in London, UK: A prospective, observational, multicentre study. Lancet Digit Health, 2022, 4(2): e117-e125.
21.	Cho S, Eom S, Kim D, et al. Artificial intelligence-derived electrocardiographic aging and risk of atrial fibrillation: A multi-national study. Eur Heart J, 2025, 46(9): 839-852.
22.	Ahmad A, Corban MT, Toya T, et al. Coronary microvascular dysfunction and the risk of atrial fibrillation from an artificial intelligence-enabled electrocardiogram. Circ Arrhythm Electrophysiol, 2021, 14(8): e009947.
23.	Bollepalli SC, Sevakula RK, Au-Yeung WTM, et al. Real-time arrhythmia detection using hybrid convolutional neural networks. J Am Heart Assoc, 2021, 10(23): e023222.
24.	Li Y, Yang W, Yu K, et al. Prediction of cardiac arrest in critically ill patients based on bedside vital signs monitoring. Comput Methods Programs Biomed, 2022, 214: 106568.
25.	Karri R, Kawai A, Thong YJ, et al. Machine learning outperforms existing clinical scoring tools in the prediction of postoperative atrial fibrillation during intensive care unit admission after cardiac surgery. Heart Lung Circ, 2021, 30(12): 1929-1937.
26.	Li F, Ren M, Pan M, et al. Risk prediction of clinical adverse outcomes with machine learning in a cohort of critically ill patients with atrial fibrillation. Sci Rep, 2021, 11(1): 1-10.
27.	Sharifi M, Buzatu D, Harris S, et al. Development of models for predicting Torsade de Pointes cardiac arrhythmias using perceptron neural networks. BMC Bioinformatics, 2017, 18(Suppl 14): 497.
28.	Mahayni AA, Attia ZI, Medina-Inojosa JR, et al. Electrocardiography-based artificial intelligence algorithm aids in prediction of long-term mortality after cardiac surgery. Mayo Clin Proc, 2021, 96(12): 3062-3070.
29.	Saeed G, Bano M, Mahmood-Khan B, et al. Continuous action deep reinforcement learning for propofol dosing during general anesthesia. Artif Intell Med, 2022, 123: 102213.
30.	Wong A, Otles E, Donnelly JP, et al. External validation of a widely implemented proprietary sepsis prediction model in hospitalized patients. JAMA Intern Med, 2021, 181(8): 1065-1070.
31.	Perez MV, Mahaffey KW, Hedlin H, et al. Large-scale assessment of a smartwatch to identify atrial fibrillation. N Engl J Med, 2019, 381(20): 1909-1917.
32.	Stehlik J, Schmalfuss C, Bozkurt B, et al. Continuous wearable monitoring analytics predict heart failure hospitalization: The LINK-HF multicenter study. Circ Heart Fail, 2020, 13(3): e006513.
33.	Hannun AY, Rajpurkar P, Haghpanahi M, et al. Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network. Nat Med, 2019, 25(1): 65-69.
34.	Zheng X, Liu Z, Liu J, et al. Advancing sports cardiology: integrating artificial intelligence with wearable devices for cardiovascular health management. ACS Appl Mater Interfaces, 2025, 17(12): 17895-17920.
35.	Jo T, Nho K, Bice P, et al. Deep learning-based identification of genetic variants: Application to Alzheimer's disease classification. Brief Bioinform, 2022, 23(2): bbac022.
36.	Elgart M, Lyons G, Romero-Brufau S, et al. Non-linear machine learning models incorporating SNPs and PRS improve polygenic prediction in diverse human populations. Commun Biol, 2022, 5(1): 856.
37.	De La Vega FM, Chowdhury S, Moore B, et al. Artificial intelligence enables comprehensive genome interpretation and nomination of candidate diagnoses for rare genetic diseases. Genome Med, 2021, 13(1): 153.
38.	Gurovich Y, Hanani Y, Bar O, et al. Identifying facial phenotypes of genetic disorders using deep learning. Nat Med, 2019, 25(1): 60-64.
39.	Kircher M, Witten DM, Jain P, et al. A general framework for estimating the relative pathogenicity of human genetic variants. Nat Genet, 2014, 46(3): 310-315.
40.	Holmgren G, Andersson P, Jakobsson A, et al. Artificial neural networks improve and simplify intensive care mortality prognostication: A national cohort study of 217, 289 first-time intensive care unit admissions. J Intensive Care, 2019, 7: 44.
41.	Zhao J, Feng Q, Wu P, et al. Learning from longitudinal data in electronic health record and genetic data to improve cardiovascular event prediction. Sci Rep, 2019, 9(1): 717.
42.	Ye C, Fu T, Hao S, et al. Prediction of incident hypertension within the next year: Prospective study using statewide electronic health records and machine learning. J Med Internet Res, 2018, 20(1): e22.
43.	Guan W, Ko D, Khurshid S, et al. Automated electronic phenotyping of cardioembolic stroke. Stroke, 2021, 52(1): 181-189.
44.	Eccles MP, Armstrong D, Baker R, et al. An implementation research agenda. Implement Sci, 2009, 4: 18.
45.	Kotecha D, Asselbergs FW, Achenbach S, et al. CODE-EHR best-practice framework for the use of structured electronic health-care records in clinical research. Lancet Digit Health, 2022, 4(10): e757-e764.
46.	Streed CG, Beach LB, Caceres BA, et al. Assessing and addressing cardiovascular health in people who are transgender and gender diverse: A scientific statement from the American Heart Association. Circulation, 2021, 144(6): e136-e148.
47.	Mu D, Bai J, Chen W, et al. Calcium scoring at coronary ct angiography using deep learning. Radiology, 2022, 302(2): 309-316.
48.	Wang YRJ, Yang K, Wen Y, et al. Screening and diagnosis of cardiovascular disease using artificial intelligence-enabled cardiac magnetic resonance imaging. Nat Med, 2024, 30(5): 1471-1480.
49.	Xu X, Jia Q, Yuan H, et al. A clinically applicable AI system for diagnosis of congenital heart diseases based on computed tomography images. Med Image Anal, 2023, 90: 102953.
50.	Cheung CY, Xu D, Cheng CY, et al. A deep-learning system for the assessment of cardiovascular disease risk via the measurement of retinal-vessel calibre. Nat Biomed Eng, 2020, 5(6): 498-508.
51.	Ma Y, Xiong J, Zhu Y, et al. Deep learning algorithm using fundus photographs for 10-year risk assessment of ischemic cardiovascular diseases in China. Sci Bull, 2022, 67(1): 17-20.
52.	Lin S, Li Z, Fu B, et al. Feasibility of using deep learning to detect coronary artery disease based on facial photo. Eur Heart J, 2020, 41(46): 4400-4411.
53.	Kung M, Zeng J, Lin S, et al. Prediction of coronary artery disease based on facial temperature information captured by non-contact infrared thermography. BMJ Health Care Inform, 2024, 31(1): e100942.
54.	Sun GH, Shen MZ, Xu WH, et al. Application of remote "Internet+" interactive mode in the management of patients with hypertension during normalized epidemic prevention and control of COVID-19. Chin J Cardiol, 2021, 49(11): 1089-1093.
55.	Tian Y, Li Z, Jin Y, et al. Foundation model of ECG diagnosis: Diagnostics and explanations of any form and rhythm on ECG. Cell Rep Med, 2024, 5(12): 101875.
56.	Liu X, Liu H, Yang G, et al. A generalist medical language model for disease diagnosis assistance. Nat Med, 2025, 31(3): 932-942.
57.	Han Y. Artificial intelligence in cardiovascular medicine in China. Eur Heart J, 2022, 43(19): 1782-1783.

1. Wu JTY, de la Hoz MÁA, Kuo PC, et al. Developing and validating multi-modal models for mortality prediction in COVID-19 patients: A multi-center retrospective study. J Digit Imaging, 2022, 35(6): 1514-1529.
2. Gehrmann S, Dernoncourt F, Li Y, et al. Comparing deep learning and concept extraction based methods for patient phenotyping from clinical narratives. PLoS One, 2018, 13(2): e0192360.
3. Komorowski M, Celi LA, Badawi O, et al. The Artificial Intelligence Clinician learns optimal treatment strategies for sepsis in intensive care. Nat Med, 2018, 24(11): 1716-1720.
4. Hu JR, Power JR, Zannad F, et al. Artificial intelligence and digital tools for design and execution of cardiovascular clinical trials. Eur Heart J, 2025, 46(9): 814-826.
5. Armoundas AA, Narayan SM, Arnett DK, et al. Use of artificial intelligence in improving outcomes in heart disease: A scientific statement from the American Heart Association. Circulation, 2024, 149(14): e1028-e1050.
6. Tatsugami F, Higaki T, Nakamura Y, et al. Deep learning-based image restoration algorithm for coronary CT angiography. Eur Radiol, 2019, 29(10): 5322-5329.
7. Howard JP, Fisher L, Shun-Shin MJ, et al. Cardiac rhythm device identification using neural networks. JACC Clin Electrophysiol, 2019, 5(5): 576-586.
8. Seetharam K, Brito D, Farjo PD, et al. The role of artificial intelligence in cardiovascular imaging: State of the art review. Front Cardiovasc Med, 2020, 7: 618849.
9. van den Oever LB, Vonder M, van Assen M, et al. Application of artificial intelligence in cardiac CT: From basics to clinical practice. Eur J Radiol, 2020, 128: 108969.
10. van Hamersvelt RW, Zreik M, Voskuil M, et al. Deep learning analysis of left ventricular myocardium in CT angiographic intermediate-degree coronary stenosis improves the diagnostic accuracy for identification of functionally significant stenosis. Eur Radiol, 2019, 29(5): 2350-2359.
11. de Vos BD, Wolterink JM, Leiner T, et al. Direct automatic coronary calcium scoring in cardiac and chest CT. IEEE Trans Med Imaging, 2019, 38(9): 2127-2138.
12. Ruijsink B, Puyol-Antón E, Oksuz I, et al. Fully automated, quality-controlled cardiac analysis from CMR: Validation and large-scale application to characterize cardiac function. JACC Cardiovasc Imaging, 2020, 13(3): 684-695.
13. Narayan SM, Wang PJ, Daubert JP. New concepts in sudden cardiac arrest to address an intractable epidemic: JACC state-of-the-art review. J Am Coll Cardiol, 2019, 73(1): 70-88.
14. Weikert T, Francone M, Abbara S, et al. Machine learning in cardiovascular radiology: ESCR position statement on design requirements, quality assessment, current applications, opportunities, and challenges. Eur Radiol, 2021, 31(6): 3909-3922.
15. Harvey H, Glocker B. A standardised approach for preparing imaging data for machine learning tasks in radiology. In: Ranschaert ER, Morozov S, Algra PR, eds. Artificial Intelligence in Medical Imaging: Opportunities, Applications and Risks. Cham: Springer, 2019: 61-72.
16. Wilkinson MD, Dumontier M, Aalbersberg IJJ, et al. The FAIR guiding principles for scientific data management and stewardship. Sci Data, 2016, 3: 160018.
17. Al-Zaiti SS, Martin-Gill C, Zègre-Hemsey JK, et al. Machine learning for ECG diagnosis and risk stratification of occlusion myocardial infarction. Nat Med, 2023, 29(7): 1804-1813.
18. Attia ZI, Noseworthy PA, Lopez-Jimenez F, et al. An artificial intelligence-enabled ECG algorithm for the identification of patients with atrial fibrillation during sinus rhythm: A retrospective analysis of outcome prediction. Lancet, 2019, 394(10201): 861-867.
19. Harmon DM, Carter RE, Cohen-Shelly M, et al. Real-world performance, long-term efficacy, and absence of bias in the artificial intelligence enhanced electrocardiogram to detect left ventricular systolic dysfunction. Eur Heart J Digit Health, 2022, 3(2): 238-244.
20. Bachtiger P, Petri CF, Scott FE, et al. Point-of-care screening for heart failure with reduced ejection fraction using artificial intelligence during ECG-enabled stethoscope examination in London, UK: A prospective, observational, multicentre study. Lancet Digit Health, 2022, 4(2): e117-e125.
21. Cho S, Eom S, Kim D, et al. Artificial intelligence-derived electrocardiographic aging and risk of atrial fibrillation: A multi-national study. Eur Heart J, 2025, 46(9): 839-852.
22. Ahmad A, Corban MT, Toya T, et al. Coronary microvascular dysfunction and the risk of atrial fibrillation from an artificial intelligence-enabled electrocardiogram. Circ Arrhythm Electrophysiol, 2021, 14(8): e009947.
23. Bollepalli SC, Sevakula RK, Au-Yeung WTM, et al. Real-time arrhythmia detection using hybrid convolutional neural networks. J Am Heart Assoc, 2021, 10(23): e023222.
24. Li Y, Yang W, Yu K, et al. Prediction of cardiac arrest in critically ill patients based on bedside vital signs monitoring. Comput Methods Programs Biomed, 2022, 214: 106568.
25. Karri R, Kawai A, Thong YJ, et al. Machine learning outperforms existing clinical scoring tools in the prediction of postoperative atrial fibrillation during intensive care unit admission after cardiac surgery. Heart Lung Circ, 2021, 30(12): 1929-1937.
26. Li F, Ren M, Pan M, et al. Risk prediction of clinical adverse outcomes with machine learning in a cohort of critically ill patients with atrial fibrillation. Sci Rep, 2021, 11(1): 1-10.
27. Sharifi M, Buzatu D, Harris S, et al. Development of models for predicting Torsade de Pointes cardiac arrhythmias using perceptron neural networks. BMC Bioinformatics, 2017, 18(Suppl 14): 497.
28. Mahayni AA, Attia ZI, Medina-Inojosa JR, et al. Electrocardiography-based artificial intelligence algorithm aids in prediction of long-term mortality after cardiac surgery. Mayo Clin Proc, 2021, 96(12): 3062-3070.
29. Saeed G, Bano M, Mahmood-Khan B, et al. Continuous action deep reinforcement learning for propofol dosing during general anesthesia. Artif Intell Med, 2022, 123: 102213.
30. Wong A, Otles E, Donnelly JP, et al. External validation of a widely implemented proprietary sepsis prediction model in hospitalized patients. JAMA Intern Med, 2021, 181(8): 1065-1070.
31. Perez MV, Mahaffey KW, Hedlin H, et al. Large-scale assessment of a smartwatch to identify atrial fibrillation. N Engl J Med, 2019, 381(20): 1909-1917.
32. Stehlik J, Schmalfuss C, Bozkurt B, et al. Continuous wearable monitoring analytics predict heart failure hospitalization: The LINK-HF multicenter study. Circ Heart Fail, 2020, 13(3): e006513.
33. Hannun AY, Rajpurkar P, Haghpanahi M, et al. Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network. Nat Med, 2019, 25(1): 65-69.
34. Zheng X, Liu Z, Liu J, et al. Advancing sports cardiology: integrating artificial intelligence with wearable devices for cardiovascular health management. ACS Appl Mater Interfaces, 2025, 17(12): 17895-17920.
35. Jo T, Nho K, Bice P, et al. Deep learning-based identification of genetic variants: Application to Alzheimer's disease classification. Brief Bioinform, 2022, 23(2): bbac022.
36. Elgart M, Lyons G, Romero-Brufau S, et al. Non-linear machine learning models incorporating SNPs and PRS improve polygenic prediction in diverse human populations. Commun Biol, 2022, 5(1): 856.
37. De La Vega FM, Chowdhury S, Moore B, et al. Artificial intelligence enables comprehensive genome interpretation and nomination of candidate diagnoses for rare genetic diseases. Genome Med, 2021, 13(1): 153.
38. Gurovich Y, Hanani Y, Bar O, et al. Identifying facial phenotypes of genetic disorders using deep learning. Nat Med, 2019, 25(1): 60-64.
39. Kircher M, Witten DM, Jain P, et al. A general framework for estimating the relative pathogenicity of human genetic variants. Nat Genet, 2014, 46(3): 310-315.
40. Holmgren G, Andersson P, Jakobsson A, et al. Artificial neural networks improve and simplify intensive care mortality prognostication: A national cohort study of 217, 289 first-time intensive care unit admissions. J Intensive Care, 2019, 7: 44.
41. Zhao J, Feng Q, Wu P, et al. Learning from longitudinal data in electronic health record and genetic data to improve cardiovascular event prediction. Sci Rep, 2019, 9(1): 717.
42. Ye C, Fu T, Hao S, et al. Prediction of incident hypertension within the next year: Prospective study using statewide electronic health records and machine learning. J Med Internet Res, 2018, 20(1): e22.
43. Guan W, Ko D, Khurshid S, et al. Automated electronic phenotyping of cardioembolic stroke. Stroke, 2021, 52(1): 181-189.
44. Eccles MP, Armstrong D, Baker R, et al. An implementation research agenda. Implement Sci, 2009, 4: 18.
45. Kotecha D, Asselbergs FW, Achenbach S, et al. CODE-EHR best-practice framework for the use of structured electronic health-care records in clinical research. Lancet Digit Health, 2022, 4(10): e757-e764.
46. Streed CG, Beach LB, Caceres BA, et al. Assessing and addressing cardiovascular health in people who are transgender and gender diverse: A scientific statement from the American Heart Association. Circulation, 2021, 144(6): e136-e148.
47. Mu D, Bai J, Chen W, et al. Calcium scoring at coronary ct angiography using deep learning. Radiology, 2022, 302(2): 309-316.
48. Wang YRJ, Yang K, Wen Y, et al. Screening and diagnosis of cardiovascular disease using artificial intelligence-enabled cardiac magnetic resonance imaging. Nat Med, 2024, 30(5): 1471-1480.
49. Xu X, Jia Q, Yuan H, et al. A clinically applicable AI system for diagnosis of congenital heart diseases based on computed tomography images. Med Image Anal, 2023, 90: 102953.
50. Cheung CY, Xu D, Cheng CY, et al. A deep-learning system for the assessment of cardiovascular disease risk via the measurement of retinal-vessel calibre. Nat Biomed Eng, 2020, 5(6): 498-508.
51. Ma Y, Xiong J, Zhu Y, et al. Deep learning algorithm using fundus photographs for 10-year risk assessment of ischemic cardiovascular diseases in China. Sci Bull, 2022, 67(1): 17-20.
52. Lin S, Li Z, Fu B, et al. Feasibility of using deep learning to detect coronary artery disease based on facial photo. Eur Heart J, 2020, 41(46): 4400-4411.
53. Kung M, Zeng J, Lin S, et al. Prediction of coronary artery disease based on facial temperature information captured by non-contact infrared thermography. BMJ Health Care Inform, 2024, 31(1): e100942.
54. Sun GH, Shen MZ, Xu WH, et al. Application of remote "Internet+" interactive mode in the management of patients with hypertension during normalized epidemic prevention and control of COVID-19. Chin J Cardiol, 2021, 49(11): 1089-1093.
55. Tian Y, Li Z, Jin Y, et al. Foundation model of ECG diagnosis: Diagnostics and explanations of any form and rhythm on ECG. Cell Rep Med, 2024, 5(12): 101875.
56. Liu X, Liu H, Yang G, et al. A generalist medical language model for disease diagnosis assistance. Nat Med, 2025, 31(3): 932-942.
57. Han Y. Artificial intelligence in cardiovascular medicine in China. Eur Heart J, 2022, 43(19): 1782-1783.

Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

Latest ArticlesInterpretation of “Use of artificial intelligence in improving outcomes in heart disease: A scientific statement from the American Heart Association” in 2024

Abstract Full text Figures/Tables Video References Cited by

Format

Content