Integration of Artificial Intelligence, Blockchain, and Wearable Technology for Chronic Disease Management: A New Paradigm in Smart Healthcare

Yi Xie , Lin Lu , Fei Gao , Shuang-jiang He , Hui-juan Zhao , Ying Fang , Jia-ming Yang , Ying An , Zhe-wei Ye , Zhe Dong

Current Medical Science ›› 2021, Vol. 41 ›› Issue (6) : 1123 -1133.

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Current Medical Science ›› 2021, Vol. 41 ›› Issue (6) : 1123 -1133. DOI: 10.1007/s11596-021-2485-0
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Integration of Artificial Intelligence, Blockchain, and Wearable Technology for Chronic Disease Management: A New Paradigm in Smart Healthcare

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Abstract

Chronic diseases are a growing concern worldwide, with nearly 25% of adults suffering from one or more chronic health conditions, thus placing a heavy burden on individuals, families, and healthcare systems. With the advent of the “Smart Healthcare” era, a series of cutting-edge technologies has brought new experiences to the management of chronic diseases. Among them, smart wearable technology not only helps people pursue a healthier lifestyle but also provides a continuous flow of healthcare data for disease diagnosis and treatment by actively recording physiological parameters and tracking the metabolic state. However, how to organize and analyze the data to achieve the ultimate goal of improving chronic disease management, in terms of quality of life, patient outcomes, and privacy protection, is an urgent issue that needs to be addressed. Artificial intelligence (AI) can provide intelligent suggestions by analyzing a patient’s physiological data from wearable devices for the diagnosis and treatment of diseases. In addition, blockchain can improve healthcare services by authorizing decentralized data sharing, protecting the privacy of users, providing data empowerment, and ensuring the reliability of data management. Integrating AI, blockchain, and wearable technology could optimize the existing chronic disease management models, with a shift from a hospital-centered model to a patient-centered one. In this paper, we conceptually demonstrate a patient-centric technical framework based on AI, blockchain, and wearable technology and further explore the application of these integrated technologies in chronic disease management. Finally, the shortcomings of this new paradigm and future research directions are also discussed.

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Yi Xie, Lin Lu, Fei Gao, Shuang-jiang He, Hui-juan Zhao, Ying Fang, Jia-ming Yang, Ying An, Zhe-wei Ye, Zhe Dong. Integration of Artificial Intelligence, Blockchain, and Wearable Technology for Chronic Disease Management: A New Paradigm in Smart Healthcare. Current Medical Science, 2021, 41(6): 1123-1133 DOI:10.1007/s11596-021-2485-0

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References

[1]

BauerUE, BrissPA, GoodmanRA, et al.. Prevention of chronic disease in the 21st century: elimination of the leading preventable causes of premature death and disability in the USA. Lancet, 2014, 384(9937): 45-52

[2]

BashshurRL, ShannonGW, SmithBR, et al.. The empirical foundations of telemedicine interventions for chronic disease management. Telemed J E Health, 2014, 20(9): 769-800

[3]

AllegranteJP, WellsMT, PetersonJC, et al.. Interventions to Support Behavioral Self-Management of Chronic Diseases. Annu Rev Public Health, 2019, 40: 127-146

[4]

KatwaU, RiveraE. Asthma Management in the Era of Smart-Medicine: Devices, Gadgets, Apps and Telemedicine. Indian J Pediatr, 2018, 85(9): 757-762

[5]

HamineS, Gerth-GuyetteE, FaulxD, et al.. Impact of mHealth chronic disease management on treatment adherence and patient outcomes: a systematic review. J Med Internet Res, 2015, 17(2): e52

[6]

SubramanianM, WojtusciszynA, FavreL, et al.. Precision medicine in the era of artificial intelligence: implications in chronic disease management. J Transl Med, 2020, 18(1): 472

[7]

ContrerasI, VehiJ. Artificial Intelligence for Diabetes Management and Decision Support: Literature Review. J Med Internet Res, 2018, 20(5): e10775

[8]

BuekersJ, TheunisJ, De BoeverP, et al.. Wearable Finger Pulse Oximetry for Continuous Oxygen Saturation Measurements During Daily Home Routines of Patients With Chronic Obstructive Pulmonary Disease (COPD) Over One Week: Observational Study. JMIR Mhealth Uhealth, 2019, 7(6): e12866

[9]

MekovE, MiravitllesM, PetkovR, et al.. Artificial intelligence and machine learning in respiratory medicine. Expert Rev Respir Med, 2020, 14(6): 559-564

[10]

SongY, MinJ, GaoW, et al.. Wearable and Implantable Electronics: Moving toward Precision Therapy. ACS Nano, 2019, 13(11): 12280-12286

[11]

CheungCC, KrahnAD, AndradeJG, et al.. The Emerging Role of Wearable Technologies in Detection of Arrhythmia. Can J Cardiol, 2018, 34(8): 1083-1087

[12]

GuoY, LiuX, ChenW, et al.. A review of wearable and unobtrusive sensing technologies for chronic disease management. Comput Biol Med, 2021, 129: 104163

[13]

LinLF, LinYJ, LinYH, et al.. Feasibility and efficacy of wearable devices for upper limb rehabilitation in patients with chronic stroke: a randomized controlled pilot study. Eur J Phys Rehabil Med, 2018, 54(3): 388-396

[14]

PilozziA, HuangX. Overcoming Alzheimer’s Disease Stigma by Leveraging Artificial Intelligence and Blockchain Technologies. Brain Sci, 2020, 10(3): 183

[15]

KuoTT, GabrielRA, Ohno-MachadoL, et al.. EXpectation Propagation LOgistic REgRession on permissioned blockCHAIN (ExplorerChain): decentralized online healthcare/genomics predictive model learning. J Am Med Inform Assoc, 2020, 27(5): 747-756

[16]

M BublitzF, OetomoA, P MoritaP, et al.. Disruptive Technologies for Environment and Health Research: An Overview of Artificial Intelligence, Blockchain, and Internet of Things. Int J Environ Res Public Health, 2019, 16(20): 3847

[17]

PeyvandiA, MajidiB, PatraJ, et al.. Computer-Aided-Diagnosis as a Service on Decentralized Medical Cloud for Efficient and Rapid Emergency Response Intelligence. New Gener Comput, 2021, 27: 1-24

[18]

SilvaP, JacobsD, NealG, et al.. Implementation of Pharmacogenomics and Artificial Intelligence Tools for Chronic Disease Management in Primary Care Setting. J Pers Med, 2021, 11(6): 443

[19]

LuL, ZhangJ, YeZ, et al.. Wearable Health Devices in Health Care: Narrative Systematic Review. JMIR Mhealth Uhealth, 2020, 8(11): e18907

[20]

JiangW, MajumderS, MondayT, et al.. A Wearable Tele-Health System towards Monitoring COVID-19 and Chronic Diseases. IEEE Rev Biomed Eng, 2021, 1: 1

[21]

DwivediAD, SrivastavaG, SinghR, et al.. A Decentralized Privacy-Preserving Healthcare Blockchain for IoT. Sensors (Basel), 2019, 19(2): 326

[22]

KalidN, ZaidanAA, MuzammilH, et al.. Based Real Time Remote Health Monitoring Systems: A Review on Patients Prioritization and Related “Big Data” Using Body Sensors information and Communication Technology. J Med Syst, 2017, 42(2): 30

[23]

QadriYA, NaumanA, KimSW, et al.. The Future of Healthcare Internet of Things: A Survey of Emerging Technologies. IEEE Communications Surveys & Tutorials, 2020, 22(2): 1121-1167

[24]

KoydemirHC, OzcanA. Wearable and Implantable Sensors for Biomedical Applications. Annu Rev Anal Chem (Palo Alto Calif), 2018, 11(1): 127-146 12

[25]

XieY, ZhangJ, WangH, et al.. Applications of Blockchain in the Medical Field: Narrative Review. J Med Internet Res, 2021, 23(10): e28613

[26]

ZhengX, SunS, Ordieres-MeréJ, et al.. Accelerating Health Data Sharing: A Solution Based on the Internet of Things and Distributed Ledger Technologies. J Med Internet Res, 2019, 21(6): e13583

[27]

KasparG, SanamK, GholkarG, et al.. Long-term use of the wearable cardioverter defibrillator in patients with explanted ICD. Int J Cardiol, 2018, 272(1): 179-184

[28]

TsukadaYT, TokitaM, IwasakiY, et al.. Validation of wearable textile electrodes for ECG monitoring. Heart Vessels, 2019, 34(7): 1203-1211

[29]

AbeY, ItoM, TanakaC, et al.. A novel and simple method using pocket-sized echocardiography to screen for aortic stenosis. J Am Soc Echocardiogr, 2013, 26: 589-596

[30]

ThoenesM, AgarwalA, GrundmannD, et al.. Narrative review of the role of artificial intelligence to improve aortic valve disease management. J Thorac Dis, 2021, 13(1): 396-404

[31]

BarrettM, BoyneJ, De WitK, et al.. Artificial intelligence supported patient self-care in chronic heart failure: a paradigm shift from reactive to predictive, preventive and personalised care. EPMA J, 2019, 10(4): 445-64

[32]

FanX, YaoQ, LiY, et al.. Multiscaled Fusion of Deep Convolutional Neural Networks for Screening Atrial Fibrillation From Single Lead Short ECG Recordings. IEEE J Biomed Health Inform, 2018, 22(6): 1744-1753

[33]

KaplanA, CaoH, KocksJWH, et al.. Artificial Intelligence/Machine Learning in Respiratory Medicine and Potential Role in Asthma and COPD Diagnosis. J Allergy Clin Immunol Pract, 2021, 9(6): 2255-2261

[34]

ColantonioS, GovoniL, VitaccaM, et al.. Decision Making Concepts for the Remote, Personalized Evaluation of COPD Patients’ Health Status. Methods Inf Med, 2015, 54(3): 240-247

[35]

BugajskiA, LengerichA, SzalachaL, et al.. Utilizing an Artificial Neural Network to Predict Self-Management in Patients With Chronic Obstructive Pulmonary Disease: An Exploratory Analysis. J Nurs Scholarsh, 2021, 53(1): 16-24

[36]

TomitaK, NagaoR, TohdaY, et al.. Deep learning facilitates the diagnosis of adult asthma. Allergol Int, 2019, 68(4): 456-461

[37]

AtherS, KadirT, GleesonF. Artificial intelligence and radiomics in pulmonary nodule management: current status and future applications. Clin Radiol, 2020, 75(1): 13-19

[38]

PorterP, AbeyratneU, DellaP, et al.. A prospective multicentre study testing the diagnostic accuracy of an automated cough sound centred analytic system for the identification of common respiratory disorders in children. Respir Res, 2019, 20(1): 81

[39]

YuG, LiZ, LiS, et al.. The role of artificial intelligence in identifying asthma in pediatric inpatient setting. Ann Transl Med, 2020, 8(21): 1367

[40]

PépinJL, BaillyS, TamisierR, et al.. Big Data in sleep apnoea: Opportunities and challenges. Respirology, 2020, 25(5): 486-494

[41]

WuCT, LiGH, ChienJY, et al.. Acute Exacerbation of a Chronic Obstructive Pulmonary Disease Prediction System Using Wearable Device Data, Machine Learning, and Deep Learning: Development and Cohort Study. JMIR Mhealth Uhealth, 2021, 9(5): e22591

[42]

Fernández-CaramésTM, Froiz-MíguezI, Blanco-NovoaO, et al.. Enabling the Internet of Mobile Crowdsourcing Health Things: A Mobile Fog Computing, Blockchain and IoT Based Continuous Glucose Monitoring System for Diabetes Mellitus Research and Care. Sensors (Basel), 2019, 19(15): 3319

[43]

HaoZ, CuiS, ZhuY, et al.. Application of non-mydriatic fundus examination and artificial intelligence to promote the screening of diabetic retinopathy in the endocrine clinic: an observational study of T2DM patients in Tianjin, China. Ther Adv Chronic Dis, 2020, 11: 2040622320942415

[44]

Mendes-SoaresH, Raveh-SadkaT, CohenY, et al.. Assessment of a Personalized Approach to Predicting Postprandial Glycemic Responses to Food Among Individuals Without Diabetes. JAMA Netw Open, 2019, 2(2): e188102

[45]

Rodriguez-LeónC, VillalongaC, Munoz-TorresM, et al.. Mobile and Wearable Technology for the Monitoring of Diabetes-Related Parameters: Systematic Review. JMIR Mhealth Uhealth, 2021, 9(6): e25138

[46]

JourdanT, DebsN, FrindelC. The Contribution of Machine Learning in the Validation of Commercial Wearable Sensors for Gait Monitoring in Patients: A Systematic Review. Sensors (Basel), 2021, 21(14): 4808

[47]

HsuWC, SugiartoT, LinYJ, et al.. Multiple-Wearable-Sensor-Based Gait Classification and Analysis in Patients with Neurological Disorders. Sensors (Basel), 2018, 18(10): 3397

[48]

ChomiakT, XianW, PeiZ, et al.. A novel single-sensor-based method for the detection of gait-cycle breakdown and freezing of gait in Parkinson’s disease. J Neural Transm (Vienna), 2019, 126(8): 1029-1036

[49]

WilliamsonJR, TelferB, MullanyR, et al.. Detecting Parkinson’s Disease from Wrist-Worn Accelerometry in the U.K. Biobank. Sensors (Basel), 2021, 21(6): 2047

[50]

NamKH, KimDH, ChoiBK, et al.. Internet of Things, Digital Biomarker, and Artificial Intelligence in Spine: Current and Future Perspectives. Neurospine, 2019, 16(4): 705-711

[51]

MeraliZG, WitiwCD, BadhiwalaJH, et al.. Using a machine learning approach to predict outcome after surgery for degenerative cervical myelopathy. PLoS One, 2019, 14(4): e0215133

[52]

GolabchiFN, SapienzaS, SeveriniG, et al.. Assessing aberrant muscle activity patterns via the analysis of surface EMG data collected during a functional evaluation. BMC Musculoskelet Disord, 2019, 20(1): 13

[53]

AraújoF, NogueiraMN, SilvaJ, et al.. A Technological-Based Platform for Risk Assessment, Detection, and Prevention of Falls Among Home-Dwelling Older Adults: Protocol for a Quasi-Experimental Study. JMIR Res Protoc, 2021, 10(8): e25781

[54]

ChaeSH, KimY, LeeKS, et al.. Development and Clinical Evaluation of a Web-Based Upper Limb Home Rehabilitation System Using a Smartwatch and Machine Learning Model for Chronic Stroke Survivors: Prospective Comparative Study. JMIR Mhealth Uhealth, 2020, 8(7): e17216

[55]

TropeaP, SchlieterH, SterpiI, et al.. Rehabilitation, the Great Absentee of Virtual Coaching in Medical Care: Scoping Review. J Med Internet Res, 2019, 21(10): e12805

[56]

ZhangH, SongC, RathoreAS, et al.. mHealth Technologies Towards Parkinson’s Disease Detection and Monitoring in Daily Life: A Comprehensive Review. IEEE Rev Biomed Eng, 2021, 14: 71-81

[57]

ZhangY, YuH, DongR, et al.. Application Prospect of Artificial Intelligence in Rehabilitation and Management of Myasthenia Gravis. Biomed Res Int, 2021, 2021: 5592472

[58]

Pareja-GaleanoH, GaratacheaN, LuciaA. Exercise as a Polypill for Chronic Diseases. Prog Mol Biol Transl Sci, 2015, 135: 497-526

[59]

Kiran MPRS, Rajalakshmi P, Bharadwaj K, et al. Adaptive rule engine based IoT enabled remote health care data acquisition and smart transmission system. 2014 IEEE World Forum on Internet of Things (WF-IoT), 2014:253–258

[60]

TanTE, AneesA, ChenC, et al.. Retinal photograph-based deep learning algorithms for myopia and a blockchain platform to facilitate artificial intelligence medical research: a retrospective multicohort study. Lancet Digit Health, 2021, 3(5): e317-e329

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