Artificial Intelligence in Medicine: Today and Tomorrow

Giovanni Briganti, Olivier Le Moine, Giovanni Briganti, Olivier Le Moine

Abstract

Artificial intelligence-powered medical technologies are rapidly evolving into applicable solutions for clinical practice. Deep learning algorithms can deal with increasing amounts of data provided by wearables, smartphones, and other mobile monitoring sensors in different areas of medicine. Currently, only very specific settings in clinical practice benefit from the application of artificial intelligence, such as the detection of atrial fibrillation, epilepsy seizures, and hypoglycemia, or the diagnosis of disease based on histopathological examination or medical imaging. The implementation of augmented medicine is long-awaited by patients because it allows for a greater autonomy and a more personalized treatment, however, it is met with resistance from physicians which were not prepared for such an evolution of clinical practice. This phenomenon also creates the need to validate these modern tools with traditional clinical trials, debate the educational upgrade of the medical curriculum in light of digital medicine as well as ethical consideration of the ongoing connected monitoring. The aim of this paper is to discuss recent scientific literature and provide a perspective on the benefits, future opportunities and risks of established artificial intelligence applications in clinical practice on physicians, healthcare institutions, medical education, and bioethics.

Keywords: artificial intelligence; digital medicine; medical technologies; mobile health; monitoring.

Copyright © 2020 Briganti and Le Moine.

References

    1. Steinhubl SR, Muse ED, Topol EJ. The emerging field of mobile health. Sci Trans Med. (2015) 7:283rv3. 10.1126/scitranslmed.aaa3487
    1. 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.2009.07.007
    1. 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-2017-204734
    1. 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.2017.04.013
    1. Topol EJ. A decade of digital medicine innovation. Sci Trans Med. (2019) 11:7610. 10.1126/scitranslmed.aaw7610
    1. 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.2018.0447
    1. 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/nyw077
    1. 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.0000000000003802
    1. 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/s11920-017-0836-0
    1. 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.2010.07.001
    1. 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/zma001189
    1. 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.2019.0095
    1. West CP, Dyrbye LN, Shanafelt TD. Physician burnout: contributors, consequences and solutions. J Int Med. (2018) 283:516–29. 10.1111/joim.12752
    1. Shah NR. Health care in 2030: will artificial intelligence replace physicians? Ann Int Med. (2019) 170:407–8. 10.7326/M19-0344
    1. Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nat Med. (2019) 25:44–56. 10.1038/s41591-018-0300-7
    1. 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.2017.19198
    1. Price WN, Gerke S, Cohen IG. Potential liability for physicians using artificial intelligence. JAMA. (2019) 322:1765–6. 10.1001/jama.2019.15064
    1. Briganti G. Nous Devons Former des Médecins ≪ augmentés ≫. Le Specialiste. (2019) Available online at: (accessed October 26, 2019).
    1. 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.117.030583
    1. 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.2018.09.002
    1. 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.
    1. 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.2017.01.001
    1. 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.116.003039
    1. 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.2019.05.014
    1. 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.
    1. Delclaux C. No need for pulmonologists to interpret pulmonary function tests. Eur Respirat J. (2019) 54:1900829. 10.1183/13993003.00829-2019
    1. 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/s12902-018-0239-1
    1. 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.2017.0087
    1. 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.2018.01.051
    1. 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.
    1. 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.2019.05.020
    1. Yang YJ, Bang CS. Application of artificial intelligence in gastroenterology. World J Gastroenterol. (2019) 25:1666–83. 10.3748/wjg.v25.i14.1666
    1. 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-0042-108434
    1. 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. 10.1097/00042737-200506000-00003
    1. 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.i37.5867
    1. 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)14568-0
    1. 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.20938
    1. 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/s00384-015-2250-6
    1. 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
    1. 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.2013.379
    1. 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.2019.02.007
    1. 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.2018.05.044
    1. 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.2018.31
    1. 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/s41591-019-0508-1
    1. 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
    1. Panch T, Mattie H, Celi LA. The inconvenient truth about AI in healthcare. NPJ Digit Med. (2019) 2:1–3. 10.1038/s41746-019-0155-4
    1. 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/s12916-019-1426-2
    1. 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
    1. Williamson JB. Preserving confidentiality and security of patient health care information. Top Health Informat Manage. (1996) 16:56–60.
    1. Montgomery J. Data sharing and the idea of ownership. New Bioeth Multidiscipl J Biotechnol Body. (2017) 23:81–6. 10.1080/20502877.2017.1314893
    1. Rodwin MA. The case for public ownership of patient data. JAMA. (2009) 302:86–8. 10.1001/jama.2009.965
    1. Mikk KA, Sleeper HA, Topol EJ. The pathway to patient data ownership and better health. JAMA. (2017) 318:1433–4. 10.1001/jama.2017.12145
    1. Brouillette M. AI added to the curriculum for doctors-to-be. Nat Med. (2019). 25:1808–9. 10.1038/s41591-019-0648-3
    1. 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.2013.2262913

Source: PubMed

3
Subscribe