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Artificial Intelligence and the Medicine of the Future

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Gerontechnology. A Clinical Perspective

Part of the book series: Practical Issues in Geriatrics ((PIG))

Abstract

The convergence of AI and machine learning (ML), electronic health records (EHRs), the Internet of Things (IoT), and enhanced data transfer and accessibility has within the last decade delivered a new paradigm in healthcare known as Healthcare 4.0. This rapid transformation to a new digital age in medicine promises new approaches to clinical diagnosis, prediction, decision-making, personalised healthcare and remote patient monitoring. In this chapter, we describe some of the history behind ML in healthcare and review the ML-Big Data nexus, the taxonomy of ML (supervised, unsupervised and reinforcement Learning), the fundamental differences between the fields of statistics and ML, the usual workflow used to develop and validate ML algorithms and how the predictive accuracy of ML algorithms is evaluated. We also discuss the barriers towards the implementation of ML algorithms for clinical decision support within healthcare including ethical considerations, data governance and security, clinician and patient confidence, transparency, data bias, as well as the issues preventing a more rapid integration of AI into healthcare. Finally, we describe some of the more recent developments of ML in healthcare, including quantum ML, federated learning, automated ML, natural language processing and the new progress made in precision medicine via a renewed focus on using reinforcement learning. Throughout the text, we try as far as possible to map the various algorithms and architectures of ML to research questions and healthcare applications with a focus on the older patient population.

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References

  1. Jayaraman PP, Forkan ARM, Morshed A, Haghighi PD, Kang Y-B. Healthcare 4.0: A review of frontiers in digital health. WIREs data mining and knowledge. Discovery. 2020;10(2):e1350.

    Google Scholar 

  2. Park SH, Do KH, Kim S, Park JH, Lim YS. What should medical students know about artificial intelligence in medicine? J Educ Eval Health Prof. 2019;16:18.

    Article  PubMed  PubMed Central  Google Scholar 

  3. Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nat Med. 2019;25(1):44–56.

    Article  CAS  PubMed  Google Scholar 

  4. Pappada SM. Machine learning in medicine: it has arrived, let's embrace it. J Card Surg. 2021;36:4121.

    Article  PubMed  Google Scholar 

  5. Garcia-Vidal C, Sanjuan G, Puerta-Alcalde P, Moreno-Garcia E, Soriano A. Artificial intelligence to support clinical decision-making processes. EBioMedicine. 2019;46:27–9.

    Article  PubMed  PubMed Central  Google Scholar 

  6. Sutton RT, Pincock D, Baumgart DC, Sadowski DC, Fedorak RN, Kroeker KI. An overview of clinical decision support systems: benefits, risks, and strategies for success. NPJ Digit Med. 2020;3:17.

    Article  PubMed  PubMed Central  Google Scholar 

  7. Hong S, Lee S, Lee J, Cha WC, Kim K. Prediction of cardiac arrest in the emergency department based on machine learning and sequential characteristics: model development and retrospective clinical validation study. JMIR Med Inform. 2020;8(8):e15932.

    Article  PubMed  PubMed Central  Google Scholar 

  8. Dykes PC, Burns Z, Adelman J, Benneyan J, Bogaisky M, Carter E, et al. Evaluation of a patient-centered fall-prevention tool kit to reduce falls and injuries: A nonrandomized controlled trial. JAMA Netw Open. 2020;3(11):e2025889.

    Article  PubMed  PubMed Central  Google Scholar 

  9. Li JJ, Jiang S, Zhu ML, Liu XH, Sun XH, Zhao SQ. Comparison of three frailty scales for prediction of adverse outcomes among older adults: A prospective cohort study. J Nutr Health Aging. 2021;25(4):419–24.

    Article  CAS  PubMed  Google Scholar 

  10. Giscombe SR, Baptiste DL, Koirala B, Asano R, Commodore-Mensah Y. The use of clinical decision support in reducing readmissions for patients with heart failure: a quasi-experimental study. Contemp Nurse. 2021;57(1–2):39–50.

    Article  PubMed  Google Scholar 

  11. Chiang J, Furler J, Boyle D, Clark M, Manski-Nankervis JA. Electronic clinical decision support tool for the evaluation of cardiovascular risk in general practice: A pilot study. Aust Fam Physician. 2017;46(10):764–8.

    PubMed  Google Scholar 

  12. Shi X, He J, Lin M, Liu C, Yan B, Song H, et al. Comparative effectiveness of team-based care with a clinical decision support system versus team-based care alone on cardiovascular risk reduction among patients with diabetes: rationale and design of the D4C trial. Am Heart J. 2021;238:45–58.

    Article  PubMed  Google Scholar 

  13. McIsaac DI, Taljaard M, Bryson GL, Beaule PE, Gagne S, Hamilton G, et al. Frailty and long-term postoperative disability trajectories: a prospective multicentre cohort study. Br J Anaesth. 2020;125(5):704–11.

    Article  PubMed  Google Scholar 

  14. Maltese G, Corsonello A, Di Rosa M, Soraci L, Vitale C, Corica F, et al. Frailty and COVID-19: A systematic scoping review. J Clin Med. 2020;9(7)

    Google Scholar 

  15. Beam AL, Kohane IS. Translating artificial intelligence into clinical care. JAMA. 2016;316(22):2368–9.

    Article  PubMed  Google Scholar 

  16. Quest D, Upjohn D, Pool E, Menaker R, Hernandez J, Poole K. Demystifying AI in healthcare: historical perspectives and current considerations. Physician Leadership. 2021;8(1):59–66.

    Google Scholar 

  17. Greco M, Caruso PF, Cecconi M. Artificial intelligence in the intensive care unit. Semin Respir Crit Care Med. 2021;42(1):2–9.

    Article  PubMed  Google Scholar 

  18. Mitchell TM. Machine learning. McGraw-Hill; 1997.

    Google Scholar 

  19. Eloranta S, Boman M. Predictive models for clinical decision making: deep dives in practical machine learning. J Intern Med. 2022;292:278.

    Article  PubMed  PubMed Central  Google Scholar 

  20. Engelhard MM, Navar AM, Pencina MJ. Incremental benefits of machine learning-when do we need a better mousetrap? JAMA Cardiol. 2021;6(6):621–3.

    Article  PubMed  Google Scholar 

  21. Alazzam MB, Mansour H, Alassery F, Almulihi A. Machine learning implementation of a diabetic patient monitoring system using interactive E-app. Comput Intell Neurosci. 2021;2021:5759184.

    Article  PubMed  PubMed Central  Google Scholar 

  22. Tomasev N, Glorot X, Rae JW, Zielinski M, Askham H, Saraiva A, et al. A clinically applicable approach to continuous prediction of future acute kidney injury. Nature. 2019;572(7767):116–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Chan L, Vaid A, Nadkarni GN. Applications of machine learning methods in kidney disease: hope or hype? Curr Opin Nephrol Hypertens. 2020;29(3):319–26.

    Article  PubMed  PubMed Central  Google Scholar 

  24. de Cock C, Milne-Ives M, van Velthoven MH, Alturkistani A, Lam C, Meinert E. Effectiveness of conversational agents (virtual assistants) in health care: protocol for a systematic review. JMIR Res Protoc. 2020;9(3):e16934.

    Article  PubMed  PubMed Central  Google Scholar 

  25. Svoboda E. Your robot surgeon will see you now. Nature. 2019;573(7775):S110–S1.

    Article  CAS  PubMed  Google Scholar 

  26. Zhou XY, Guo Y, Shen M, Yang GZ. Application of artificial intelligence in surgery. Front Med. 2020;14(4):417–30.

    Article  PubMed  Google Scholar 

  27. Nasr M, Islam M, Shehata S, Karray F, Quintana Y. Smart healthcare in the age of AI: recent advances, challenges, and future prospects. 2021.

    Google Scholar 

  28. Rajkomar A, Oren E, Chen K, Dai AM, Hajaj N, Hardt M, et al. Scalable and accurate deep learning with electronic health records. NPJ Digit Med. 2018;1:18.

    Article  PubMed  PubMed Central  Google Scholar 

  29. Ayers B, Sandholm T, Gosev I, Prasad S, Kilic A. Using machine learning to improve survival prediction after heart transplantation. J Card Surg. 2021;36(11):4113–20.

    Article  PubMed  Google Scholar 

  30. Crotti N. Startup Arterys wins FDA clearance for AI-assisted cardiac imaging system. 2017.

    Google Scholar 

  31. Leiner T, Rueckert D, Suinesiaputra A, Baessler B, Nezafat R, Isgum I, et al. Machine learning in cardiovascular magnetic resonance: basic concepts and applications. J Cardiovasc Magn Reson. 2019;21(1):61.

    Article  PubMed  PubMed Central  Google Scholar 

  32. Akkus Z, Galimzianova A, Hoogi A, Rubin DL, Erickson BJ. Deep learning for brain MRI segmentation: state of the art and future directions. J Digit Imaging. 2017;30(4):449–59.

    Article  PubMed  PubMed Central  Google Scholar 

  33. Esteva A, Kuprel B, Novoa RA, Ko J, Swetter SM, Blau HM, et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature. 2017;542(7639):115–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Weng SF, Reps J, Kai J, Garibaldi JM, Qureshi N. Can machine-learning improve cardiovascular risk prediction using routine clinical data? PLoS One. 2017;12(4):e0174944.

    Article  PubMed  PubMed Central  Google Scholar 

  35. Inglese M, Patel N, Linton-Reid K, Loreto F, Win Z, Perry RJ, et al. A predictive model using the mesoscopic architecture of the living brain to detect Alzheimer’s disease. Commun Med (Lond). 2022;2:70.

    Article  PubMed  Google Scholar 

  36. Ehteshami Bejnordi B, Veta M, Johannes van Diest P, van Ginneken B, Karssemeijer N, Litjens G, et al. Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer. JAMA. 2017;318(22):2199–210.

    Article  PubMed  PubMed Central  Google Scholar 

  37. Krause J, Gulshan V, Rahimy E, Karth P, Widner K, Corrado GS, et al. Grader variability and the importance of reference standards for evaluating machine learning models for diabetic retinopathy. Ophthalmology. 2018;125(8):1264–72.

    Article  PubMed  Google Scholar 

  38. Byrne MF, Chapados N, Soudan F, Oertel C, Linares Perez M, Kelly R, et al. Real-time differentiation of adenomatous and hyperplastic diminutive colorectal polyps during analysis of unaltered videos of standard colonoscopy using a deep learning model. Gut. 2019;68(1):94–100.

    Article  PubMed  Google Scholar 

  39. Hassan C, Spadaccini M, Iannone A, Maselli R, Jovani M, Chandrasekar VT, et al. Performance of artificial intelligence in colonoscopy for adenoma and polyp detection: a systematic review and meta-analysis. Gastrointest Endosc. 2021;93(1):77–85 e6.

    Article  PubMed  Google Scholar 

  40. Moyo S, Doan TN, Yun JA, Tshuma N. Application of machine learning models in predicting length of stay among healthcare workers in underserved communities in South Africa. Hum Resour Health. 2018;16(1):68.

    Article  PubMed  PubMed Central  Google Scholar 

  41. Jiang D, Hao M, Ding F, Fu J, Li M. Mapping the transmission risk of Zika virus using machine learning models. Acta Trop. 2018;185:391–9.

    Article  PubMed  Google Scholar 

  42. Huang SW, Tsai HP, Hung SJ, Ko WC, Wang JR. Assessing the risk of dengue severity using demographic information and laboratory test results with machine learning. PLoS Negl Trop Dis. 2020;14(12):e0008960.

    Article  PubMed  PubMed Central  Google Scholar 

  43. Hinton G. Deep learning-A technology with the potential to transform health care. JAMA. 2018;320(11):1101–2.

    Article  PubMed  Google Scholar 

  44. Kooi T, Litjens G, van Ginneken B, Gubern-Merida A, Sanchez CI, Mann R, et al. Large scale deep learning for computer aided detection of mammographic lesions. Med Image Anal. 2017;35:303–12.

    Article  PubMed  Google Scholar 

  45. Dinov ID, Heavner B, Tang M, Glusman G, Chard K, Darcy M, et al. Predictive big data analytics: A study of Parkinson’s disease using large, complex, heterogeneous, incongruent, multi-Source and incomplete observations. PLoS One. 2016;11(8):e0157077.

    Article  PubMed  PubMed Central  Google Scholar 

  46. Benke K, Benke G. Artificial intelligence and big data in public health. Int J Environ Res Public Health. 2018;15(12)

    Google Scholar 

  47. Beam AL, Kohane IS. Big data and machine learning in health care. JAMA. 2018;319(13):1317–8.

    Article  PubMed  Google Scholar 

  48. Doust JA, Bonner C, Bell KJL. Future directions in cardiovascular disease risk prediction. Aust J Gen Pract. 2020;49(8):488–94.

    Article  PubMed  Google Scholar 

  49. Gulshan V, Peng L, Coram M, Stumpe MC, Wu D, Narayanaswamy A, et al. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA. 2016;316(22):2402–10.

    Article  PubMed  Google Scholar 

  50. Yones SA, Annett A, Stoll P, Diamanti K, Holmfeldt L, Barrenas CF, et al. Interpretable machine learning identifies paediatric systemic lupus erythematosus subtypes based on gene expression data. Sci Rep. 2022;12(1):7433.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Sarker IH. AI-based modeling: techniques, applications and research issues towards automation, intelligent and smart systems. SN Comput Sci. 2022;3(2):158.

    Article  PubMed  PubMed Central  Google Scholar 

  52. Jiang F, Jiang Y, Zhi H, Dong Y, Li H, Ma S, et al. Artificial intelligence in healthcare: past, present and future. Stroke Vasc Neurol. 2017;2(4):230–43.

    Article  PubMed  PubMed Central  Google Scholar 

  53. Alexander G, Bahja M, Butt GF. Automating large-scale health care service feedback analysis: sentiment analysis and topic modeling study. JMIR Med Inform. 2022;10(4):e29385.

    Article  PubMed  PubMed Central  Google Scholar 

  54. Woodman RJ, Bryant K, Sorich MJ, Pilotto A, Mangoni AA. Use of multiprognostic index domain scores, clinical data, and machine learning to improve 12-month mortality risk prediction in older hospitalized patients: prospective cohort study. J Med Internet Res. 2021;23(6):e26139.

    Article  PubMed  PubMed Central  Google Scholar 

  55. Kautzky A, Moller HJ, Dold M, Bartova L, Seemuller F, Laux G, et al. Combining machine learning algorithms for prediction of antidepressant treatment response. Acta Psychiatr Scand. 2021;143(1):36–49.

    Article  CAS  PubMed  Google Scholar 

  56. Mangoni AA, Woodman RJ. The potential value of person-centred statistical methods in ageing research. Age Ageing. 2019;48(6):783–4.

    PubMed  Google Scholar 

  57. Mangoni AA, Woodman RJ, Piga M, Cauli A, Fedele AL, Gremese E, et al. Patterns of anti-inflammatory and Immunomodulating drug usage and microvascular endothelial function in rheumatoid arthritis. Front Cardiovasc Med. 2021;8:681327.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Woodman RJ, Wood KM, Kunnel A, Dedigama M, Pegoli MA, Soiza RL, et al. Patterns of drug use and serum sodium concentrations in older hospitalized patients: A latent class analysis approach. Drugs Real World Outcomes. 2016;3(4):383–91.

    Article  PubMed  PubMed Central  Google Scholar 

  59. Woodman RJ, Baghdadi LR, Shanahan EM, de Silva I, Hodgson JM, Mangoni AA. Diets high in n-3 fatty acids are associated with lower arterial stiffness in patients with rheumatoid arthritis: a latent profile analysis. Br J Nutr. 2019;121(2):182–94.

    Article  CAS  PubMed  Google Scholar 

  60. Arulkumaran K, Deisenroth MP, Brundage M, Bharath AA. Deep reinforcement learning: a brief survey. EEE Signal Process Mag. 2017;34(Nov):26–38.

    Article  Google Scholar 

  61. Jonsson A. Deep reinforcement learning in medicine. Kidney Dis (Basel). 2019;5(1):18–22.

    Article  PubMed  Google Scholar 

  62. Zhang Z. When doctors meet with AlphaGo: potential application of machine learning to clinical medicine. Ann Transl Med. 2016;4(6):125.

    Article  PubMed  PubMed Central  Google Scholar 

  63. Silver D, Huang A, Maddison CJ, Guez A, Sifre L, van den Driessche G, et al. Mastering the game of go with deep neural networks and tree search. Nature. 2016;529(7587):484–9.

    Article  CAS  PubMed  Google Scholar 

  64. Source GI. Google buys Deepmind for $500 million; 2022. Available from: https://guruitsource.com/google-buys-deepmind-for-500-million/.

  65. Colleran K, DeepMind AI. Lab predicts structure of most proteins; 2022. Available from: https://techilive.in/deepmind-ai-lab-predicts-structure-of-most-proteins/.

  66. Liu S, See KC, Ngiam KY, Celi LA, Sun X, Feng M. Reinforcement learning for clinical decision support in critical care: comprehensive review. J Med Internet Res. 2020;22(7):e18477.

    Article  PubMed  PubMed Central  Google Scholar 

  67. Nowakowski K, El Kirat K, Dao TT. Deep reinforcement learning coupled with musculoskeletal modelling for a better understanding of elderly falls. Med Biol Eng Comput. 2022;60(6):1745–61.

    Article  PubMed  Google Scholar 

  68. Shah H. The DeepMind debacle demands dialogue on data. Nature. 2017;547(7663):259.

    Article  CAS  PubMed  Google Scholar 

  69. Powles J, Hodson H. Google DeepMind and healthcare in an age of algorithms. Health Technol (Berl). 2017;7(4):351–67.

    Article  PubMed  Google Scholar 

  70. Morley J, Taddeo M, Floridi L. Google health and the NHS: overcoming the trust deficit. Lancet Digit Health. 2019;1(8):e389.

    Article  PubMed  Google Scholar 

  71. McGraw D, Mandl KD. Privacy protections to encourage use of health-relevant digital data in a learning health system. NPJ Digit Med. 2021;4(1):2.

    Article  PubMed  PubMed Central  Google Scholar 

  72. Grote T, Berens P. On the ethics of algorithmic decision-making in healthcare. J Med Ethics. 2020;46(3):205–11.

    Article  PubMed  Google Scholar 

  73. Baird A, Schuller B. Considerations for a more ethical approach to data in AI: on data representation and infrastructure. Front Big Data. 2020;3:25.

    Article  PubMed  PubMed Central  Google Scholar 

  74. Harwich E, Laycock K. Thinking on its own: AI in the NHS. Reform Res Trust. 2018;

    Google Scholar 

  75. Scheetz J, Rothschild P, McGuinness M, Hadoux X, Soyer HP, Janda M, et al. A survey of clinicians on the use of artificial intelligence in ophthalmology, dermatology, radiology and radiation oncology. Sci Rep. 2021;11(1):5193.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  76. Cristiano A, Musteata S, De Silvestri S, Bellandi V, Ceravolo P, Cesari M, et al. Older Adults’ and Clinicians’ perspectives on a smart health platform for the aging population: design and evaluation study. JMIR Aging. 2022;5(1):e29623.

    Article  PubMed  PubMed Central  Google Scholar 

  77. Bzdok D, Altman N, Krzywinski M. Statistics versus machine learning. Nat Methods. 2018;15(4):233–4.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  78. Lipton Z. The mythos of model interprtability. ACM Queue. 2018;16:31–57.

    Article  Google Scholar 

  79. Mohar C. Interpretable machine learning: a guide for making black box models explainable. [GitHub Repository] 2019. Available from: https://christophm.github.io/interpretable-ml-book/example-based.html.

  80. Amann J, Blasimme A, Vayena E, Frey D, Madai VI, Qc P. Explainability for artificial intelligence in healthcare: a multidisciplinary perspective. BMC Med Inform Decis Mak. 2020;20(1):310.

    Article  PubMed  PubMed Central  Google Scholar 

  81. Vokinger KN, Feuerriegel S, Kesselheim AS. Mitigating bias in machine learning for medicine. Commun Med (London). 2021;1:25.

    Article  Google Scholar 

  82. Informatics OHDSa. OHDSI; 2022. Available from: https://www.ohdsi.org/ohdsi-workgroups/.

  83. (MACH) MACfH. Transformational data collaboration; 2020. Available from: https://machaustralia.org/projects/transformational-data-collaboration/.

  84. Arute F, Arya K, Babbush R, Bacon D, Bardin JC, Barends R, et al. Quantum supremacy using a programmable superconducting processor. Nature. 2019;574(7779):505–10.

    Article  CAS  PubMed  Google Scholar 

  85. Solenov D, Brieler J, Scherrer JF. The potential of quantum computing and machine learning to advance clinical research and change the practice of medicine. Mo Med. 2018;115(5):463–7.

    PubMed  PubMed Central  Google Scholar 

  86. Rieke N, Hancox J, Li W, Milletari F, Roth HR, Albarqouni S, et al. The future of digital health with federated learning. NPJ Digit Med. 2020;3:119.

    Article  PubMed  PubMed Central  Google Scholar 

  87. Waring J, Lindvall C, Umeton R. Automated machine learning: review of the state-of-the-art and opportunities for healthcare. Artif Intell Med. 2020;104:101822.

    Article  PubMed  Google Scholar 

  88. Cloud G. AutoML; 2022. Available from: https://cloud.google.com/automl.

  89. He X, Zhao K, Chu X. AutoML: A survey of the state-of-the-art. Knowl-Based Syst. 2021;212:106662.

    Article  Google Scholar 

  90. Kong HJ. Managing unstructured big data in healthcare system. Healthc Inform Res. 2019;25(1):1–2.

    Article  PubMed  PubMed Central  Google Scholar 

  91. Wouters OJ, McKee M, Luyten J. Estimated research and development investment needed to bring a new medicine to market, 2009–2018. JAMA. 2020;323(9):844–53.

    Article  PubMed  PubMed Central  Google Scholar 

  92. Vatansever S, Schlessinger A, Wacker D, Kaniskan HU, Jin J, Zhou MM, et al. Artificial intelligence and machine learning-aided drug discovery in central nervous system diseases: state-of-the-arts and future directions. Med Res Rev. 2021;41(3):1427–73.

    Article  PubMed  Google Scholar 

  93. Ahmed F, Soomro AM, Chethikkattuveli Salih AR, Samantasinghar A, Asif A, Kang IS, et al. A comprehensive review of artificial intelligence and network based approaches to drug repurposing in Covid-19. Biomed Pharmacother. 2022;153:113350.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  94. Sharma PP, Bansal M, Sethi A, Poonam PL, Goel VK, et al. Computational methods directed towards drug repurposing for COVID-19: advantages and limitations. RSC Advances. 2021;11(57):36181–98.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  95. Lv H, Shi L, Berkenpas JW, Dao F-Y, Zulfiqar H, Ding H, et al. Application of artificial intelligence and machine learning for COVID-19 drug discovery and vaccine design. Brief Bioinform. 2021;22(6)

    Google Scholar 

  96. Konig IR, Fuchs O, Hansen G, von Mutius E, Kopp MV. What is precision medicine? Eur Respir J. 2017;50(4):1700391.

    Article  PubMed  Google Scholar 

  97. McGrath S, Ghersi D. Building towards precision medicine: empowering medical professionals for the next revolution. BMC Med Genet. 2016;9(1):23.

    Google Scholar 

  98. Hingorani AD, Windt DA, Riley RD, Abrams K, Moons KG, Steyerberg EW, et al. Prognosis research strategy (PROGRESS) 4: stratified medicine research. BMJ. 2013;346:e5793.

    Article  PubMed  PubMed Central  Google Scholar 

  99. Gutierrez-Valencia M, Izquierdo M, Cesari M, Casas-Herrero A, Inzitari M, Martinez-Velilla N. The relationship between frailty and polypharmacy in older people: a systematic review. Br J Clin Pharmacol. 2018;84(7):1432–44.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  100. Coronato A, Naeem M, De Pietro G, Paragliola G. Reinforcement learning for intelligent healthcare applications: a survey. Artif Intell Med. 2020;109:101964.

    Article  PubMed  Google Scholar 

  101. Zheng H, Zhu J, Xie W, Zhong J. Reinforcement learning assisted oxygen therapy for COVID-19 patients under intensive care. BMC Med Inform Decis Mak. 2021;21(1):350.

    Article  PubMed  PubMed Central  Google Scholar 

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Woodman, R., Mangoni, A.A. (2023). Artificial Intelligence and the Medicine of the Future. In: Pilotto, A., Maetzler, W. (eds) Gerontechnology. A Clinical Perspective. Practical Issues in Geriatrics. Springer, Cham. https://doi.org/10.1007/978-3-031-32246-4_12

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