دورية أكاديمية

Novel Artificial Intelligence Applications in Cardiology: Current Landscape, Limitations, and the Road to Real-World Applications.

التفاصيل البيبلوغرافية
العنوان: Novel Artificial Intelligence Applications in Cardiology: Current Landscape, Limitations, and the Road to Real-World Applications.
المؤلفون: Langlais ÉL; Division of Cardiology, Department of Medicine, Montreal Heart Institute, University of Montreal, 5000 Belanger Street, Montreal, QC, H1T 1C8, Canada.; Biomedical Engineering, École Polytechnique de Montréal, 2500 Chemin de Polytechnique, Montreal, QC, H3T 1J4, Canada., Thériault-Lauzier P; Division of Cardiology, Department of Medicine, University of Ottawa Heart Institute, University of Ottawa, 40 Ruskin Street, Ottawa, ON, K1Y 4W7, Canada., Marquis-Gravel G; Division of Cardiology, Department of Medicine, Montreal Heart Institute, University of Montreal, 5000 Belanger Street, Montreal, QC, H1T 1C8, Canada.; Department of Medicine, University of Montreal, 2900 Edouard Montpetit Blvd, Montreal, QC, H3T 1J4, Canada., Kulbay M; Department of Medicine, University of Montreal, 2900 Edouard Montpetit Blvd, Montreal, QC, H3T 1J4, Canada., So DY; Division of Cardiology, Department of Medicine, University of Ottawa Heart Institute, University of Ottawa, 40 Ruskin Street, Ottawa, ON, K1Y 4W7, Canada., Tanguay JF; Division of Cardiology, Department of Medicine, Montreal Heart Institute, University of Montreal, 5000 Belanger Street, Montreal, QC, H1T 1C8, Canada., Ly HQ; Division of Cardiology, Department of Medicine, Montreal Heart Institute, University of Montreal, 5000 Belanger Street, Montreal, QC, H1T 1C8, Canada., Gallo R; Division of Cardiology, Department of Medicine, Montreal Heart Institute, University of Montreal, 5000 Belanger Street, Montreal, QC, H1T 1C8, Canada.; Department of Medicine, University of Montreal, 2900 Edouard Montpetit Blvd, Montreal, QC, H3T 1J4, Canada., Lesage F; Division of Cardiology, Department of Medicine, Montreal Heart Institute, University of Montreal, 5000 Belanger Street, Montreal, QC, H1T 1C8, Canada.; Biomedical Engineering, École Polytechnique de Montréal, 2500 Chemin de Polytechnique, Montreal, QC, H3T 1J4, Canada., Avram R; Division of Cardiology, Department of Medicine, Montreal Heart Institute, University of Montreal, 5000 Belanger Street, Montreal, QC, H1T 1C8, Canada. robert.avram.md@gmail.com.; Department of Medicine, University of Montreal, 2900 Edouard Montpetit Blvd, Montreal, QC, H3T 1J4, Canada. robert.avram.md@gmail.com.
المصدر: Journal of cardiovascular translational research [J Cardiovasc Transl Res] 2023 Jun; Vol. 16 (3), pp. 513-525. Date of Electronic Publication: 2022 Apr 22.
نوع المنشور: Journal Article; Review
اللغة: English
بيانات الدورية: Publisher: Springer Country of Publication: United States NLM ID: 101468585 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1937-5395 (Electronic) Linking ISSN: 19375387 NLM ISO Abbreviation: J Cardiovasc Transl Res Subsets: MEDLINE
أسماء مطبوعة: Original Publication: New York, NY : Springer
مواضيع طبية MeSH: Cardiology* , Cardiovascular Diseases*/diagnosis , Cardiovascular Diseases*/therapy, Humans ; Artificial Intelligence ; Algorithms ; Precision Medicine
مستخلص: Cardiovascular diseases are the leading cause of death globally and contribute significantly to the cost of healthcare. Artificial intelligence (AI) is poised to reshape cardiology. Using supervised and unsupervised learning, the two main branches of AI, several applications have been developed in recent years to improve risk prediction, allow large-scale analysis of medical data, and phenotype patients for personalized medicine. In this review, we examine the key advances in AI in cardiology and its limitations regarding bias in the data, standardization in reporting, data access, and model trust and accountability in cases of error. Finally, we discuss implementation methods to unleash AI's potential in making healthcare more accurate and efficient. Several steps need to be followed and challenges overcome in order to successfully integrate AI in clinical practice and ensure its longevity.
(© 2022. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)
References: W. H. Organization. “Cardiovascular diseases,” February 12, 2022; https://www.who.int/health-topics/cardiovascular-diseases#tab=tab_1 .
Hannun, A. Y., Rajpurkar, P., Haghpanahi, M., Tison, G. H., Bourn, C., Turakhia, M. P., & Ng, A. Y. (2019). Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network. Nature Medicine, 25(1), 65–69. (PMID: 10.1038/s41591-018-0268-3306173206784839)
Hughes, J. W., Olgin, J. E., Avram, R., Abreau, S. A., Sittler, T., Radia, K., Hsia, H., Walters, T., Lee, B., Gonzalez, J. E., & Tison, G. H. (2021). Performance of a convolutional neural network and explainability technique for 12-lead electrocardiogram interpretation. JAMA Cardiology, 6(11), 1285–1295. (PMID: 10.1001/jamacardio.2021.2746343470078340011)
Avram, R., Olgin, J. E., Kuhar, P., Hughes, J. W., Marcus, G. M., Pletcher, M. J., Aschbacher, K., & Tison, G. H. (2020). A digital biomarker of diabetes from smartphone-based vascular signals. Nature Medicine, 26(10), 1576–1582. (PMID: 10.1038/s41591-020-1010-5328079318483886)
Attia, Z. I., Kapa, S., Lopez-Jimenez, F., Mckie, P. M., Ladewig, D. J., Satam, G., Pellikka, P. A., Enriquez-Sarano, M., Noseworthy, P. A., Munger, T. M., Asirvatham, S. J., Scott, C. G., Carter, R. E., & Friedman, P. A. (2019). Screening for cardiac contractile dysfunction using an artificial intelligence–enabled electrocardiogram. Nature Medicine, 25(1), 70–74. (PMID: 10.1038/s41591-018-0240-230617318)
Yao, X., Rushlow, D. R., Inselman, J. W., Mccoy, R. G., Thacher, T. D., Behnken, E. M., Bernard, M. E., Rosas, S. L., Akfaly, A., Misra, A., Molling, P. E., Krien, J. S., Foss, R. M., Barry, B. A., Siontis, K. C., Kapa, S., Pellikka, P. A., Lopez-Jimenez, F., Attia, Z. I., et al. (2021). Artificial intelligence–enabled electrocardiograms for identification of patients with low ejection fraction: A pragmatic, randomized clinical trial. Nature Medicine, 27(5), 815–819. (PMID: 10.1038/s41591-021-01335-433958795)
Janiesch, C., Zschech, P., & Heinrich, K. (2021). Machine learning and deep learning. Electronic Markets, 31, 685–695. (PMID: 10.1007/s12525-021-00475-2)
Johnson, K. W., Soto, J. T., Glicksberg, B. S., Shameer, K., Miotto, R., Ali, M., Ashley, E., & Dudley, J. T. (2018). Artificial intelligence in cardiology. Journal of the American College of Cardiology, 71(23), 2668–2679. (PMID: 10.1016/j.jacc.2018.03.52129880128)
Itchhaporia, D. (2022). Artificial intelligence in cardiology. Trends Cardiovasc Med, 32(1), 34–41. (PMID: 10.1016/j.tcm.2020.11.00733242635)
Rajpurkar, P., Chen, E., Banerjee, O., & Topol, E. J. (2022). AI in health and medicine. Nature Medicine, 20.
Dosovitskiy, A, Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., and Houlsby, N. (2020). “An image is worth 16x16 words: Transformers for image recognition at scale,” 2020-10-22T17:55:59.
Shah, S. J., Katz, D. H., Selvaraj, S., Burke, M. A., Yancy, C. W., Gheorghiade, M., Bonow, R. O., Huang, C.-C., & Deo, R. C. (2015). Phenomapping for novel classification of heart failure with preserved ejection fraction. Circulation, 131(3), 269–279. (PMID: 10.1161/CIRCULATIONAHA.114.01063725398313)
Ouali, Y., Hudelot, C, and Tami, M. (2020). “An overview of deep semi-supervised learning,” 2020-07-06T17:38:19.
Yu, C., Liu, J., and Nemati, S. (2020) “Reinforcement learning in healthcare: A survey,” 2020-04-24T14:45:14.
Attia, Z. I., Noseworthy, P. A., Lopez-Jimenez, F., Asirvatham, S. J., Deshmukh, A. J., Gersh, B. J., Carter, R. E., Yao, X., Rabinstein, A. A., Erickson, B. J., Kapa, S., & Friedman, P. A. (2019). An artificial intelligence-enabled ECG algorithm for the identification of patients with atrial fibrillation during sinus rhythm: A retrospective analysis of outcome prediction. Lancet, 394(10201), 861–867. (PMID: 10.1016/S0140-6736(19)31721-031378392)
Raghunath, S., Pfeifer, J. M., Ulloa-Cerna, A. E., Nemani, A., Carbonati, T., Jing, L., Vanmaanen, D. P., Hartzel, D. N., Ruhl, J. A., Lagerman, B. F., Rocha, D. B., Stoudt, N. J., Schneider, G., Johnson, K. W., Zimmerman, N., Leader, J. B., Kirchner, H. L., Griessenauer, C. J., Hafez, A., et al. (2021). Deep neural networks can predict new-onset atrial fibrillation from the 12-lead ECG and help identify those at risk of atrial fibrillation–related stroke. Circulation, 143(13), 1287–1298. (PMID: 10.1161/CIRCULATIONAHA.120.047829335885847996054)
Bachtiger, P., Petri, C. F., Scott, F.E., Ri Park, S., Kelshiker, M. A., Sahemey, H. K., Dumea, B., Alquero, R., Padam, P.S., Hatrick, I. R., Ali, A., Ribeiro, M., Cheung, W.-S., Bual, N., Rana, B., Shun-Shin, M., Kramer, D. B., Fragoyannis, A., Keene, D., Plymen, C. M., and Peters, N. S. (2022) “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,” The Lancet Digital Health, 2022/01/05/.
Eng, D., Chute, C., Khandwala, N., Rajpurkar, P., Long, J., Shleifer, S., Khalaf, M. H., Sandhu, A. T., Rodriguez, F., Maron, D. J., Seyyedi, S., Marin, D., Golub, I., Budoff, M., Kitamura, F., Takahashi, M. S., Filice, R. W., Shah, R., Mongan, J., et al. (2021). Automated coronary calcium scoring using deep learning with multicenter external validation. npj Digital Medicine, 4(1).
Pickhardt, P. J., Graffy, P. M., Zea, R., Lee, S. J., Liu, J., Sandfort, V., & Summers, R. M. (2020). Automated CT biomarkers for opportunistic prediction of future cardiovascular events and mortality in an asymptomatic screening population: A retrospective cohort study. The Lancet Digital Health, 2(4), e192–e200. (PMID: 10.1016/S2589-7500(20)30025-X328645987454161)
Zeleznik, R., Foldyna, B., Eslami, P., Weiss, J., Alexander, I., Taron, J., Parmar, C., Alvi, R. M., Banerji, D., Uno, M., Kikuchi, Y., Karady, J., Zhang, L., Scholtz, J.-E., Mayrhofer, T., Lyass, A., Mahoney, T. F., Massaro, J. M., Vasan, R. S., et al. (2021). Deep convolutional neural networks to predict cardiovascular risk from computed tomography. Nature Communications, 12, 1. (PMID: 10.1038/s41467-021-20966-2)
Zhang, J., Gajjala, S., Agrawal, P., Tison, G. H., Hallock, L. A., Beussink-Nelson, L., Lassen, M. H., Fan, E., Aras, M. A., Jordan, C., Fleischmann, K. E., Melisko, M., Qasim, A., Shah, S. J., Bajcsy, R., & Deo, R. C. (2018). Fully automated echocardiogram interpretation in clinical practice. Circulation, 138(16), 1623–1635. (PMID: 10.1161/CIRCULATIONAHA.118.034338303544596200386)
Ghorbani, A., Ouyang, D., Abid, A., He, B., Chen, J. H., Harrington, R. A., Liang, D. H., Ashley, E. A., & Zou, J. Y. (2020). Deep learning interpretation of echocardiograms. npj Digital Medicine, 3, 1. (PMID: 10.1038/s41746-019-0216-8)
Arnaout, R., Curran, L., Zhao, Y., Levine, J. C., Chinn, E., & Moon-Grady, A. J. (2021). An ensemble of neural networks provides expert-level prenatal detection of complex congenital heart disease. Nat Med, 27(5), 882–891. (PMID: 10.1038/s41591-021-01342-5339908068380434)
Hughes, J. W., Yuan, N., He, B., Ouyang, J., Ebinger, J., Botting, P., Lee, J., Theurer, J., Tooley, J. E., Neiman, K., Lungren, M. P., Liang, D., Schnittger, I., Harrington, B., Chen, J. H., Ashley, E. A., Cheng, S., Ouyang, D., & Zou, J. Y. (2021). Deep learning prediction of biomarkers from echocardiogram videos. Cold Spring Harbor Laboratory. (PMID: 10.1101/2021.02.03.21251080)
Du, T., Xie, L., Zhang, H., Liu, X., Wang, X., Chen, D., Xu, Y., Sun, Z., Zhou, W., Song, L., Guan, C., Lansky, A. J., & Xu, B. (2021). Training and validation of a deep learning architecture for the automatic analysis of coronary angiography. EuroIntervention, 17(1), 32–40. (PMID: 10.4244/EIJ-D-20-00570328306479753915)
Avram, R, Olgin, J. E., Wan, A., Ahmed, Z., Verreault-Julien, L., Abreau, S., Wan, D., Gonzalez, J.E, So, D. Y., Soni, K., and Tison, G. H. (2021). “CathAI: Fully automated interpretation of coronary angiograms using neural networks,” 2021-06-14T18:58:09.
Fearon, W. F., Achenbach, S., Engstrom, T., Assali, A., Shlofmitz, R., Jeremias, A., Fournier, S., Kirtane, A. J., Kornowski, R., Greenberg, G., Jubeh, R., Kolansky, D. M., Mcandrew, T., Dressler, O., Maehara, A., Matsumura, M., Leon, M. B., & De Bruyne, B. (2019). Accuracy of fractional flow reserve derived from coronary angiography. Circulation, 139(4), 477–484. (PMID: 10.1161/CIRCULATIONAHA.118.03735030586699)
Howard, J. P., Fisher, L., Shun-Shin, M. J., Keene, D., Arnold, A. D., Ahmad, Y., Cook, C. M., Moon, J. C., Manisty, C. H., Whinnett, Z. I., Cole,G. D., Rueckert, D., and Francis, D. P., “Cardiac rhythm device identification using neural networks,” no. 2405-5018 (Electronic).
Kim, C., Lee, G., Oh, H., Jeong, G., Kim, S. W., Chun, E. J., Kim, Y.-H., Lee, J.-G., and Yang, D. H. (2021). “A deep learning–based automatic analysis of cardiovascular borders on chest radiographs of valvular heart disease: Development/external validation,” European Radiology, 2021-10-13.
Ueda, D., Yamamoto, A., Ehara, S., Iwata, S., Abo, K., Walston, S. L., Matsumoto, T., Shimazaki, A., Yoshiyama, M., and Miki, Y. (2021). “Artificial intelligence-based detection of aortic stenosis from chest radiographs,” European Heart Journal - Digital Health, 2021-12-07.
Perez, M. V., Mahaffey, K. W., Hedlin, H., Rumsfeld, J. S., Garcia, A., Ferris, T., Balasubramanian, V., Russo, A. M., Rajmane, A., Cheung, L., Hung, G., Lee, J., Kowey, P., Talati, N., Nag, D., Gummidipundi, S. E., Beatty, A., Hills, M. T., Desai, S., et al. (2019). Large-scale assessment of a smartwatch to identify atrial fibrillation. New England Journal of Medicine, 381(20), 1909–1917. (PMID: 10.1056/NEJMoa190118331722151)
Avram, R., Ramsis, M., Cristal, A. D., Nathan, V., Zhu, L., Kim, J., Kuang, J., Gao, A., Vittinghoff, E., Rohdin-Bibby, L., Yogi, S., Seremet, E., Carp, V., Badilini, F., Pletcher, M. J., Marcus, G. M., Mortara, D., & Olgin, J. E. (2021). Validation of an algorithm for continuous monitoring of atrial fibrillation using a consumer smartwatch. Heart Rhythm, 18(9), 1482–1490. (PMID: 10.1016/j.hrthm.2021.03.04433838317)
Tazarv, A., & Levorato, M. (2021). A deep learning approach to predict blood pressure from PPG signals. Annu Int Conf IEEE Eng Med Biol Soc, 2021, 5658–5662. (PMID: 34892406)
Hurley, N. C., Desai, N., Dhruva, S. S., Khera, R., Schulz, W., Huang, C., Curtis, J., Masoudi, F., Rumsfeld, J., Negahban, S., Krumholz, H. M., & Mortazavi, B. J. (2021). A dynamic model to estimate evolving risk of major bleeding after percutaneous coronary intervention. Cold Spring Harbor Laboratory. (PMID: 10.1101/2021.12.17.21267935)
Lee, J., Yoon, W., Kim, S., Kim, D., Kim, S., So, C. H., Kang, J., & Wren, J. (2019). BioBERT: A pre-trained biomedical language representation model for biomedical text mining. Bioinformatics, 1–7.
Karwath, A., Bunting, K. V., Gill, S. K., Tica, O., Pendleton, S., Aziz, F., Barsky, A. D., Chernbumroong, S., Duan, J., Mobley, A. R., Cardoso, V. R., Slater, L., Williams, J. A., Bruce, E. J., Wang, X., Flather, M. D., Coats, A. J. S., Gkoutos, G. V., & Kotecha, D. (2021). Redefining β-blocker response in heart failure patients with sinus rhythm and atrial fibrillation: A machine learning cluster analysis. Lancet, 398(10309), 1427–1435. (PMID: 10.1016/S0140-6736(21)01638-X344740118542730)
Chen,M., Wang, G. J., Chen, H., and Ding, Z. J. (2020). “Adaptive region aggregation network: Unsupervised domain adaptation with adversarial training for ECG delineation,” 2020 Ieee International Conference on Acoustics, Speech, and Signal Processing, pp. 1274-1278.
Masutani, E. M., Bahrami, N., & Hsiao, A. (2020). Deep learning single-frame and multiframe super-resolution for cardiac MRI. Radiology, 295(3), 552–561. (PMID: 10.1148/radiol.202019217332286192)
Roy, M. S., Gupta, R., and Das Sharma, K. (2020) “Photoplethysmogram signal quality evaluation by unsupervised learning approach,” Proceedings of 2020 Ieee Applied Signal Processing Conference (Aspcon 2020), pp. 6-10.
Hongo, R. H., & Goldschlager, N. (2006). Status of computerized electrocardiography. Cardiol Clin, 24(3), 491–504. (PMID: 10.1016/j.ccl.2006.03.00516939838)
Saporta, A., Gui, X., Agrawal, A., Pareek, A., Truong, S. Q. H., Nguyen, C. D. T., Ngo, V.-D., Seekins, J., Blankenberg, F. G., Ng, A. Y., Lungren, M. P., and Rajpurkar, P. (2021) “Benchmarking saliency methods for chest X-ray interpretation,” medRxiv, pp. 2021.02.28.21252634.
Ehsan, U., Passi, S., Liao, Q. V., Chan, L., Lee, I.-H., Muller, M., and Riedl, M. O. (2021). “The who in explainable AI: How AI background shapes perceptions of AI explanations,” 2021-07-28T17:32:04.
Wolf, P. A., Abbott, R. D., & Kannel, W. B. (1991). Atrial fibrillation as an independent risk factor for stroke: The Framingham Study. Stroke, 22(8), 983–988. (PMID: 10.1161/01.STR.22.8.9831866765)
Galloway, C. D., Valys, A. V., Shreibati, J. B., Treiman, D. L., Petterson, F. L., Gundotra, V. P., Albert, D. E., Attia, Z. I., Carter, R. E., Asirvatham, S. J., Ackerman, M. J., Noseworthy, P. A., Dillon, J. J., & Friedman, P. A. (2019). Development and validation of a deep-learning model to screen for hyperkalemia from the electrocardiogram. JAMA Cardiology, 4(5), 428. (PMID: 10.1001/jamacardio.2019.0640309428456537816)
Kwon, J. M., Lee, S. Y., Jeon, K. H., Lee, Y., Kim, K. H., Park, J., Oh, B. H., & Lee, M. M. (2020). Deep learning–based algorithm for detecting aortic stenosis using electrocardiography. Journal of the American Heart Association, 9(7).
Tison, G. H., Zhang, J., Delling, F. N., & Deo, R. C. (2019). Automated and interpretable patient ECG profiles for disease detection, tracking, and discovery. Circulation: Cardiovascular Quality and Outcomes, 12(9).
Kwon, J.-M., Jeon, K.-H., Kim, H. M., Kim, M. J., Lim, S. M., Kim, K.-H., Song, P. S., Park, J., Choi, R. K., & Oh, B.-H. (2020). Comparing the performance of artificial intelligence and conventional diagnosis criteria for detecting left ventricular hypertrophy using electrocardiography. EP Europace, 22(3), 412–419. (PMID: 10.1093/europace/euz324)
U. N. S. C. o. t. E. o. A. Radiation, “UNSCEAR 2000 Report to the General Assembly, with scientific annexes,” Sources and effects of ionizing radiation. New York: United Nations, 2000.
Allen, J. (2007). Photoplethysmography and its application in clinical physiological measurement. Physiol Meas, 28(3), R1–R39. (PMID: 10.1088/0967-3334/28/3/R0117322588)
Samsung. “How to measure blood pressure with Galaxy Watch Active2 and Watch3,” 2021; https://www.samsung.com/sg/support/mobile-devices/how-to-measure-blood-pressure-with-galaxy-watch/ .
Huang, Z, Long, G., Wessler, B., and Hughes, M. C. (2021). “A new semi-supervised learning benchmark for classifying view and diagnosing aortic stenosis from echocardiograms,” 2021-07-30T21:08:12.
Madani, A., Ong, J. R., Tibrewal, A., & Mofrad, M. R. K. (2018). Deep echocardiography: Data-efficient supervised and semi-supervised deep learning towards automated diagnosis of cardiac disease. npj Digital Medicine, 1, 1. (PMID: 10.1038/s41746-018-0065-x)
Sheller, M. J., Edwards, B., Reina, G. A., Martin, J., Pati, S., Kotrotsou, A., Milchenko, M., Xu, W., Marcus, D., Colen, R. R., and Bakas, S. (2020). “Federated learning in medicine: Facilitating multi-institutional collaborations without sharing patient data,” Scientific Reports, 10, (1), 2020-12-01.
Murphy, K., Di Ruggiero, E., Upshur, R., Willison, D. J., Malhotra, N., Cai, J. C., Malhotra, N., Lui, V., & Gibson, J. (2021). Artificial intelligence for good health: A scoping review of the ethics literature. Bmc Medical Ethics, 22(1).
Maxwell, Y. L.. “AI in cardiology: Where we are now and where to go next,” https://www.tctmd.com/news/ai-cardiology-where-we-are-now-and-where-go-next .
Cruz Rivera, S., Liu, X., Chan, A.-W., Denniston, A. K., Calvert, M. J., Darzi, A., Holmes, C., Yau, C., Moher, D., Ashrafian, H., Deeks, J. J., Ferrante Di Ruffano, L., Faes, L., Keane, P. A., Vollmer, S. J., Lee, A. Y., Jonas, A., Esteva, A., Beam, A. L., et al. (2020). Guidelines for clinical trial protocols for interventions involving artificial intelligence: The SPIRIT-AI extension. Nature Medicine, 26(9), 1351–1363. (PMID: 10.1038/s41591-020-1037-7329082847598944)
I. B. Committee., Report of the IBC on big data and health 0000248724, 2017.
Shawe-Taylor, J., and Williamson, R. C. (1997). “A PAC analysis of a Bayesian estimator,” in ACM Press the tenth annual conference, Nashville, Tennessee, United States, pp. 2–9.
Wiens, J., Saria, S., Sendak, M., Ghassemi, M., Liu, V. X., Doshi-Velez, F., Jung, K., Heller, K., Kale, D., Saeed, M., Ossorio, P. N., Thadaney-Israni, S., & Goldenberg, A. (2019). Do no harm: A roadmap for responsible machine learning for health care. Nature Medicine, 25(9), 1337–1340. (PMID: 10.1038/s41591-019-0548-631427808)
Gerke, S. M., T.; Cohen, G. (2020) “Ethical and legal challenges of artificial intelligence-driven healthcare,” Artificial Intelligence in Healthcare pp. 295–336.
Griffiths, S. (2018). “The big ethical questions for artificial intelligence (AI) in healthcare,”.
Diethe, T, Borchert, T, Thereska, E, Balle, B., and Lawrence, N (2019). “Continual learning in practice,” arXiv preprint arXiv:1903.05202.
Caccia, L., and Pineau, J. (2021). “SPeCiaL: Self-Supervised Pretraining for Continual Learning.
Weinreich, M., Chudow, J. J., Weinreich, B., Krumerman, T., Nag, T., Rahgozar, K., Shulman, E., Fisher, J., & Ferrick, K. J. (2019). Development of an artificially intelligent mobile phone application to identify cardiac devices on chest radiography. JACC: Clinical Electrophysiology, 5(9), 1094–1095. (PMID: 31537342)
فهرسة مساهمة: Keywords: Algorithms; Artificial intelligence; Cardiology; Cardiovascular care; Coronary angiogram; Diagnosis; Echocardiogram; Electrocardiogram; Real-world applications; Screening
تواريخ الأحداث: Date Created: 20220423 Date Completed: 20230629 Latest Revision: 20230629
رمز التحديث: 20240628
DOI: 10.1007/s12265-022-10260-x
PMID: 35460017
قاعدة البيانات: MEDLINE
الوصف
تدمد:1937-5395
DOI:10.1007/s12265-022-10260-x