Steinhubl SR, Muse ED, Topol EJ. The emerging field of mobile health. Sci Trans Med. (2015) 7:283rv3. 10.1126/scitranslmed.aaa348725877894
Peng Y, Zhang Y, Wang L. Artificial intelligence in biomedical engineering and informatics: an introduction and review. Artif Intell Med. (2010) 48:71–3. 10.1016/j.artmed.2009.07.00720045299
Orth M, Averina M, Chatzipanagiotou S, Faure G, Haushofer A, Kusec V, et al. Opinion: redefining the role of the physician in laboratory medicine in the context of emerging technologies, personalised medicine and patient autonomy ('4P medicine'). J Clin Pathol. (2019) 72:191–7. 10.1136/jclinpath-2017-20473429273576
Abdulnabi M, Al-Haiqi A, Kiah MLM, Zaidan AA, Zaidan BB, Hussain M. A distributed framework for health information exchange using smartphone technologies. J Biomed Informat. (2017) 69:230–50. 10.1016/j.jbi.2017.04.01328433825
Topol EJ. A decade of digital medicine innovation. Sci Trans Med. (2019) 11:7610. 10.1126/scitranslmed.aaw761031243153
Morawski K, Ghazinouri R, Krumme A, Lauffenburger JC, Lu Z, Durfee E, et al. Association of a smartphone application with medication adherence and blood pressure control: the MedISAFE-BP randomized clinical trial. JAMA Int Med. (2018) 178:802–9. 10.1001/jamainternmed.2018.044729710289
Overley SC, Cho SK, Mehta AI, Arnold PM. Navigation and robotics in spinal surgery: where are we now? Neurosurgery. (2017) 80:S86–99. 10.1093/neuros/nyw07728350944
Tepper OM, Rudy HL, Lefkowitz A, Weimer KA, Marks SM, Stern CS, et al. Mixed reality with HoloLens: where virtual reality meets augmented reality in the operating room. Plast Reconstruct Surg. (2017) 140:1066–70. 10.1097/PRS.000000000000380229068946
Mishkind MC, Norr AM, Katz AC, Reger GM. Review of virtual reality treatment in psychiatry: evidence versus current diffusion and use. Curr Psychiat Rep. (2017) 19:80. 10.1007/s11920-017-0836-028920179
Malloy KM, Milling LS. The effectiveness of virtual reality distraction for pain reduction: a systematic review. Clin Psychol Rev. (2010) 30:1011–8. 10.1016/j.cpr.2010.07.00120691523
Haag M, Igel C, Fischer MR, German Medical Education Society (GMA) Digitization-Technology-Assisted Learning and Teaching joint working group Technology-enhanced Teaching and Learning in Medicine (TeLL) of the german association for medical informatics biometry and epidemiology (gmds) and the German Informatics Society (GI). Digital teaching and digital medicine: a national initiative is needed. GMS J Med Educ. (2018) 35:Doc43. 10.3205/zma00118930186953
Chaiyachati KH, Shea JA, Asch DA, Liu M, Bellini LM, Dine CJ, et al. Assessment of inpatient time allocation among first-year internal medicine residents using time-motion observations. JAMA Int Med. (2019) 179:760–7. 10.1001/jamainternmed.2019.009530985861
West CP, Dyrbye LN, Shanafelt TD. Physician burnout: contributors, consequences and solutions. J Int Med. (2018) 283:516–29. 10.1111/joim.1275229505159
Shah NR. Health care in 2030: will artificial intelligence replace physicians? Ann Int Med. (2019) 170:407–8. 10.7326/M19-034430802901
Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nat Med. (2019) 25:44–56. 10.1038/s41591-018-0300-730617339
Verghese A, Shah NH, Harrington RA. What this computer needs is a physician: humanism and artificial intelligence. JAMA. (2018) 319:19–20. 10.1001/jama.2017.1919829261830
Price WN, Gerke S, Cohen IG. Potential liability for physicians using artificial intelligence. JAMA. (2019) 322:1765–6. 10.1001/jama.2019.15064
Briganti G. Nous Devons Former des Médecins ≪ augmentés ≫. Le Specialiste. (2019) Available online at: https://www.lespecialiste.be/fr/debats/nous-devons-former-des-medecins-laquo-nbsp-augmentes-raquo.html (accessed October 26, 2019).
Halcox JPJ, Wareham K, Cardew A, Gilmore M, Barry JP, Phillips C, et al. Assessment of remote heart rhythm sampling using the AliveCor heart monitor to screen for atrial fibrillation: the REHEARSE-AF study. Circulation. (2017) 136:1784–94. 10.1161/CIRCULATIONAHA.117.03058328851729
Turakhia MP, Desai M, Hedlin H, Rajmane A, Talati N, Ferris T, et al. Rationale and design of a large-scale, app-based study to identify cardiac arrhythmias using a smartwatch: the apple heart study. Ame Heart J. (2019) 207:66–75. 10.1016/j.ahj.2018.09.00230392584
Raja JM, Elsakr C, Roman S, Cave B, Pour-Ghaz I, Nanda A, et al. Apple watch, wearables, and heart rhythm: where do we stand? Ann Trans Med. (2019) 7:417. 10.21037/atm.2019.06.79.31660316
Huang Z, Chan TM, Dong W. MACE prediction of acute coronary syndrome via boosted resampling classification using electronic medical records. J Biomed Inform. (2017) 66:161–70. 10.1016/j.jbi.2017.01.00128065840
Mortazavi BJ, Downing NS, Bucholz EM, Dharmarajan K, Manhapra A, Li SX, et al. Analysis of machine learning techniques for heart failure readmissions. Circ Cardiovasc Qual Outcomes. (2016) 9:629–40. 10.1161/CIRCOUTCOMES.116.00303928263938
Dorado-Díaz PI, Sampedro-Gómez J, Vicente-Palacios V, Sánchez PL. Applications of artificial intelligence in cardiology. The future is already here. Revista Española de Cardiología. (2019) 72:1065–75. 10.1016/j.rec.2019.05.01431611150
Topalovic M, Das N, Burgel PR, Daenen M, Derom E, Haenebalcke C, et al. Artificial intelligence outperforms pulmonologists in the interpretation of pulmonary function tests. Eur Respirat J. (2019) 53:1801660. 10.1183/13993003.01660-2018.30765505
Delclaux C. No need for pulmonologists to interpret pulmonary function tests. Eur Respirat J. (2019) 54:1900829. 10.1183/13993003.00829-201931320479
Lawton J, Blackburn M, Allen J, Campbell F, Elleri D, Leelarathna L, et al. Patients' and caregivers' experiences of using continuous glucose monitoring to support diabetes self-management: qualitative study. BMC Endocrine Disord. (2018) 18:12. 10.1186/s12902-018-0239-129458348
Christiansen MP, Garg SK, Brazg R, Bode BW, Bailey TS, Slover RH, et al. Accuracy of a fourth-generation subcutaneous continuous glucose sensor. Diabet Technol Therapeut. (2017) 19:446–56. 10.1089/dia.2017.008728700272
Niel O, Boussard C, Bastard P. Artificial intelligence can predict GFR decline during the course of ADPKD. Am J Kidney Dis Off J Natl Kidney Found. (2018) 71:911–2. 10.1053/j.ajkd.2018.01.05129609979
Geddes CC, Fox JG, Allison ME, Boulton-Jones JM, Simpson K. An artificial neural network can select patients at high risk of developing progressive IgA nephropathy more accurately than experienced nephrologists. Nephrol Dialysis, Transplant. (1998) 13:67–71. 9481717
Niel O, Bastard P. Artificial intelligence in nephrology: core concepts, clinical applications, and perspectives. Am J Kidney Dis. (2019) 74:803–10. 10.1053/j.ajkd.2019.05.02031451330
Yang YJ, Bang CS. Application of artificial intelligence in gastroenterology. World J Gastroenterol. (2019) 25:1666–83. 10.3748/wjg.v25.i14.166631011253
Fernández-Esparrach G, Bernal J, López-Cerón M, Córdova H, Sánchez-Montes C, Rodríguez de Miguel C, et al. Exploring the clinical potential of an automatic colonic polyp detection method based on the creation of energy maps. Endoscopy. (2016) 48:837–42. 10.1055/s-0042-10843427285900
Pace F, Buscema M, Dominici P, Intraligi M, Baldi F, Cestari R, et al. Artificial neural networks are able to recognize gastro-oesophageal reflux disease patients solely on the basis of clinical data. Eur J Gastroenterol Hepatol. (2005) 17:605–10. 10.1097/00042737-200506000-0000315879721
Lahner E, Grossi E, Intraligi M, Buscema M, Corleto VD, Delle Fave G, et al. Possible contribution of artificial neural networks and linear discriminant analysis in recognition of patients with suspected atrophic body gastritis. World J Gastroenterol. (2005) 11:5867–73. 10.3748/wjg.v11.i37.586716270400
Das A, Ben-Menachem T, Cooper GS, Chak A, Sivak MV, Gonet JA, et al. Prediction of outcome in acute lower-gastrointestinal haemorrhage based on an artificial neural network: internal and external validation of a predictive model. Lancet. (2003) 362:1261–6. 10.1016/S0140-6736(03)14568-014575969
Sato F, Shimada Y, Selaru FM, Shibata D, Maeda M, Watanabe G, et al. Prediction of survival in patients with esophageal carcinoma using artificial neural networks. Cancer. (2005) 103:1596–605. 10.1002/cncr.2093815751017
Peng JC, Ran ZH, Shen J. Seasonal variation in onset and relapse of IBD and a model to predict the frequency of onset, relapse, and severity of IBD based on artificial neural network. Int J Colorect Dis. (2015) 30:1267–73. 10.1007/s00384-015-2250-625976931
Ichimasa K, Kudo SE, Mori Y, Misawa M, Matsudaira S, Kouyama Y, et al. Artificial intelligence may help in predicting the need for additional surgery after endoscopic resection of T1 colorectal cancer. Endoscopy. (2018) 50:230–40. 10.1055/s-0043-122385
Yang HX, Feng W, Wei JC, Zeng TS, Li ZD, Zhang LJ, et al. Support vector machine-based nomogram predicts postoperative distant metastasis for patients with oesophageal squamous cell carcinoma. Br J Cancer. (2013) 109:1109–16. 10.1038/bjc.2013.37923942069
Regalia G, Onorati F, Lai M, Caborni C, Picard RW. Multimodal wrist-worn devices for seizure detection and advancing research: focus on the Empatica wristbands. Epilep Res. (2019) 153:79–82. 10.1016/j.eplepsyres.2019.02.00730846346
Bruno E, Simblett S, Lang A, Biondi A, Odoi C, Schulze-Bonhage A, et al. Wearable technology in epilepsy: the views of patients, caregivers, and healthcare professionals. Epilep Behav. (2018) 85:141–9. 10.1016/j.yebeh.2018.05.04429940377
Dorsey ER, Glidden AM, Holloway MR, Birbeck GL, Schwamm LH. Teleneurology and mobile technologies: the future of neurological care. Nat Rev Neurol. (2018) 14:285–97. 10.1038/nrneurol.2018.3129623949
Campanella G, Hanna MG, Geneslaw L, Miraflor A, Silva VWK, Busam KJ, et al. Clinical-grade computational pathology using weakly supervised deep learning on whole slide images. Nat Med. (2019) 25:1301–9. 10.1038/s41591-019-0508-131308507
Liu X, Faes L, Kale AU, Wagner SK, Fu DJ, Bruynseels A, et al. A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: a systematic review and meta-analysis. Lancet Digit Health. (2019) 1:e271–97. 10.1016/S2589-7500(19)30123-2
Panch T, Mattie H, Celi LA. The inconvenient truth about AI in healthcare. NPJ Digit Med. (2019) 2:1–3. 10.1038/s41746-019-0155-431453372
Kelly CJ, Karthikesalingam A, Suleyman M, Corrado G, King D. Key challenges for delivering clinical impact with artificial intelligence. BMC Med. (2019) 17:195. 10.1186/s12916-019-1426-231665002
Mittelstadt B. Ethics of the health-related internet of things: a narrative review. Ethics Informat Technol. (2017) 19:157–75. 10.1007/s10676-017-9426-4
Williamson JB. Preserving confidentiality and security of patient health care information. Top Health Informat Manage. (1996) 16:56–60. 10157662
Montgomery J. Data sharing and the idea of ownership. New Bioeth Multidiscipl J Biotechnol Body. (2017) 23:81–6. 10.1080/20502877.2017.131489328517982
Rodwin MA. The case for public ownership of patient data. JAMA. (2009) 302:86–8. 10.1001/jama.2009.96519567445
Mikk KA, Sleeper HA, Topol EJ. The pathway to patient data ownership and better health. JAMA. (2017) 318:1433–4. 10.1001/jama.2017.1214528973063
Brouillette M. AI added to the curriculum for doctors-to-be. Nat Med. (2019). 25:1808–9. 10.1038/s41591-019-0648-331806886
Acampora G, Cook DJ, Rashidi P, Vasilakos AV. A survey on ambient intelligence in health care. Proc IEEE Inst Elect Electron Eng. (2013) 101:2470–94. 10.1109/JPROC.2013.226291324431472