1. Blackburn H, Keys A, Simonson E, Rautaharju P, Punsar S. The electrocardiogram in population studies. A classification system. Circulation. 1960; 21:1160–1175. PMID:
13849070.
2. Henkens IR, Gan CT, van Wolferen SA, et al. ECG monitoring of treatment response in pulmonary arterial hypertension patients. Chest. 2008; 134:1250–1257. PMID:
18641107.
3. Levy D, Salomon M, D'Agostino RB, Belanger AJ, Kannel WB. Prognostic implications of baseline electrocardiographic features and their serial changes in subjects with left ventricular hypertrophy. Circulation. 1994; 90:1786–1793. PMID:
7923663.
4. Tonelli AR, Baumgartner M, Alkukhun L, Minai OA, Dweik RA. Electrocardiography at diagnosis and close to the time of death in pulmonary arterial hypertension. Ann Noninvasive Electrocardiol. 2014; 19:258–265. PMID:
24372670.
5. 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; 394:861–867. PMID:
31378392.
6. Takeuchi I, Fujita H, Ohe K, et al. Initial experience of mobile cloud ECG system contributing to the shortening of door to balloon time in an acute myocardial infarction patient. Int Heart J. 2013; 54:45–47. PMID:
23428924.
7. Ip JE. Wearable devices for cardiac rhythm diagnosis and management. JAMA. 2019; 321:337–338. PMID:
30633301.
8. Hsieh JC, Hsu MW. A cloud computing based 12-lead ECG telemedicine service. BMC Med Inform Decis Mak. 2012; 12:77. PMID:
22838382.
9. Zakka P, Refaat MM. A scoring algorithm in wide complex tachycardia: Ventricular tachycardia or not ventricular tachycardia? Pacing Clin Electrophysiol. 2019; 42:634–636. PMID:
30903697.
10. Wolf M, Krause J, Carney PA, Bogart A, Kurvers RH. Collective intelligence meets medical decision-making: the collective outperforms the best radiologist. PLoS One. 2015; 10:e0134269. PMID:
26267331.
11. Heylighen F. Collective intelligence and its implementation on the web: algorithms to develop a collective mental map. Comput Math Organ Theory. 1999; 5:253–280.
12. Radcliffe K, Lyson HC, Barr-Walker J, Sarkar U. Collective intelligence in medical decision-making: a systematic scoping review. BMC Med Inform Decis Mak. 2019; 19:158. PMID:
31399099.
13. 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; 25:65–69. PMID:
30617320.
14. Taye GT, Shim EB, Hwang HJ, Lim KM. Machine learning approach to predict ventricular fibrillation based on QRS complex shape. Front Physiol. 2019; 10:1193. PMID:
31616311.
15. Javadi M, Ebrahimpour R, Sajedin A, Faridi S, Zakernejad S. Improving ECG classification accuracy using an ensemble of neural network modules. PLoS One. 2011; 6:e24386. PMID:
22046232.
16. Schmidt C. M. D. Anderson breaks with IBM Watson, raising questions about artificial intelligence in oncology. J Natl Cancer Inst. 2017; 109:109.
17. Tacchella A, Romano S, Ferraldeschi M, et al. Collaboration between a human group and artificial intelligence can improve prediction of multiple sclerosis course: a proof-of-principle study. F1000 Res. 2017; 6:2172.
18. Desai V, Dave D. Is artificial intelligence better than manual methods in diagnosis of electrocardiograms (ECGs) or not? Int J Adv Med. 2017; 4:1463–1465.