Skip to main content
PMC home page
Springer Nature - PMC COVID-19 Collection logoLink to Springer Nature - PMC COVID-19 Collection
. 2021 Dec 24;41(6):1123–1133. doi: 10.1007/s11596-021-2485-0

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

Yi Xie 1,2,#, Lin Lu 1,2,#, Fei Gao 1,2, Shuang-jiang He 2,3, Hui-juan Zhao 2,3, Ying Fang 2, Jia-ming Yang 2, Ying An 2,4, Zhe-wei Ye 1,2, Zhe Dong 5,✉
PMCID: PMC8702375  PMID: 34950987

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.

Key words: artificial intelligence, blockchain, wearable technology/devices, chronic diseases, smart healthcare, health monitoring, personalization, healthcare management, patient-centric

Footnotes

This work was supported by the National Natural Science Foundation of China (No. 81974355 and No. 82172525), the National Intelligence Medical Clinical Research Center (No. 2020021105012440), and the Hubei Province Technology Innovation Major Special Project (No. 2018AAA067).

Conflict of Interest Statement

The authors declare that they have no conflicts of interest.

These authors contributed equally to this work and should be considered as co-first authors.

Contributor Information

Yi Xie, Email: 455085617@qq.com.

Lin Lu, Email: lledu2014@163.com.

Zhe Dong, Email: 13601706191@139.com.

References

  • 1.Bauer UE, Briss PA, Goodman RA, 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. doi: 10.1016/S0140-6736(14)60648-6. [DOI] [PubMed] [Google Scholar]
  • 2.Bashshur RL, Shannon GW, Smith BR, et al. The empirical foundations of telemedicine interventions for chronic disease management. Telemed J E Health. 2014;20(9):769–800. doi: 10.1089/tmj.2014.9981. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 3.Allegrante JP, Wells MT, Peterson JC, et al. Interventions to Support Behavioral Self-Management of Chronic Diseases. Annu Rev Public Health. 2019;40:127–146. doi: 10.1146/annurev-publhealth-040218-044008. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 4.Katwa U, Rivera E. Asthma Management in the Era of Smart-Medicine: Devices, Gadgets, Apps and Telemedicine. Indian J Pediatr. 2018;85(9):757–762. doi: 10.1007/s12098-018-2611-6. [DOI] [PubMed] [Google Scholar]
  • 5.Hamine S, Gerth-Guyette E, Faulx D, 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. doi: 10.2196/jmir.3951. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 6.Subramanian M, Wojtusciszyn A, Favre L, et al. Precision medicine in the era of artificial intelligence: implications in chronic disease management. J Transl Med. 2020;18(1):472. doi: 10.1186/s12967-020-02658-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 7.Contreras I, Vehi J. Artificial Intelligence for Diabetes Management and Decision Support: Literature Review. J Med Internet Res. 2018;20(5):e10775. doi: 10.2196/10775. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8.Buekers J, Theunis J, De Boever P, 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. doi: 10.2196/12866. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 9.Mekov E, Miravitlles M, Petkov R, et al. Artificial intelligence and machine learning in respiratory medicine. Expert Rev Respir Med. 2020;14(6):559–564. doi: 10.1080/17476348.2020.1743181. [DOI] [PubMed] [Google Scholar]
  • 10.Song Y, Min J, Gao W, et al. Wearable and Implantable Electronics: Moving toward Precision Therapy. ACS Nano. 2019;13(11):12280–12286. doi: 10.1021/acsnano.9b08323. [DOI] [PubMed] [Google Scholar]
  • 11.Cheung CC, Krahn AD, Andrade JG, et al. The Emerging Role of Wearable Technologies in Detection of Arrhythmia. Can J Cardiol. 2018;34(8):1083–1087. doi: 10.1016/j.cjca.2018.05.003. [DOI] [PubMed] [Google Scholar]
  • 12.Guo Y, Liu X, Chen W, et al. A review of wearable and unobtrusive sensing technologies for chronic disease management. Comput Biol Med. 2021;129:104163. doi: 10.1016/j.compbiomed.2020.104163. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13.Lin LF, Lin YJ, Lin YH, 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. doi: 10.23736/S1973-9087.17.04691-3. [DOI] [PubMed] [Google Scholar]
  • 14.Pilozzi A, Huang X. Overcoming Alzheimer’s Disease Stigma by Leveraging Artificial Intelligence and Blockchain Technologies. Brain Sci. 2020;10(3):183. doi: 10.3390/brainsci10030183. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 15.Kuo TT, Gabriel RA, Ohno-Machado L, 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. doi: 10.1093/jamia/ocaa023. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 16.M Bublitz F, Oetomo A, P Morita P, 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. doi: 10.3390/ijerph16203847. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 17.Peyvandi A, Majidi B, Patra J, 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. doi: 10.1007/s00354-021-00131-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 18.Silva P, Jacobs D, Neal G, et al. Implementation of Pharmacogenomics and Artificial Intelligence Tools for Chronic Disease Management in Primary Care Setting. J Pers Med. 2021;11(6):443. doi: 10.3390/jpm11060443. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 19.Lu L, Zhang J, Ye Z, et al. Wearable Health Devices in Health Care: Narrative Systematic Review. JMIR Mhealth Uhealth. 2020;8(11):e18907. doi: 10.2196/18907. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 20.Jiang W, Majumder S, Monday T, et al. A Wearable Tele-Health System towards Monitoring COVID-19 and Chronic Diseases. IEEE Rev Biomed Eng. 2021;1:1. doi: 10.1109/RBME.2021.3069815. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 21.Dwivedi AD, Srivastava G, Singh R, et al. A Decentralized Privacy-Preserving Healthcare Blockchain for IoT. Sensors (Basel) 2019;19(2):326. doi: 10.3390/s19020326. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22.Kalid N, Zaidan AA, Muzammil H, 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. doi: 10.1007/s10916-017-0883-4. [DOI] [PubMed] [Google Scholar]
  • 23.Qadri YA, Nauman A, Kim SW, et al. The Future of Healthcare Internet of Things: A Survey of Emerging Technologies. IEEE Communications Surveys & Tutorials. 2020;22(2):1121–1167. doi: 10.1109/COMST.2020.2973314. [DOI] [Google Scholar]
  • 24.Koydemir HC, Ozcan A. Wearable and Implantable Sensors for Biomedical Applications. Annu Rev Anal Chem (Palo Alto Calif) 2018;11(1):127–146. doi: 10.1146/annurev-anchem-061417-125956. [DOI] [PubMed] [Google Scholar]
  • 25.Xie Y, Zhang J, Wang H, et al. Applications of Blockchain in the Medical Field: Narrative Review. J Med Internet Res. 2021;23(10):e28613. doi: 10.2196/28613. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 26.Zheng X, Sun S, Ordieres-Meré J, 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. doi: 10.2196/13583. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 27.Kaspar G, Sanam K, Gholkar G, et al. Long-term use of the wearable cardioverter defibrillator in patients with explanted ICD. Int J Cardiol. 2018;272(1):179–184. doi: 10.1016/j.ijcard.2018.08.017. [DOI] [PubMed] [Google Scholar]
  • 28.Tsukada YT, Tokita M, Iwasaki Y, et al. Validation of wearable textile electrodes for ECG monitoring. Heart Vessels. 2019;34(7):1203–1211. doi: 10.1007/s00380-019-01347-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 29.Abe Y, Ito M, Tanaka C, et al. A novel and simple method using pocket-sized echocardiography to screen for aortic stenosis. J Am Soc Echocardiogr. 2013;26:589–596. doi: 10.1016/j.echo.2013.03.008. [DOI] [PubMed] [Google Scholar]
  • 30.Thoenes M, Agarwal A, Grundmann D, et al. Narrative review of the role of artificial intelligence to improve aortic valve disease management. J Thorac Dis. 2021;13(1):396–404. doi: 10.21037/jtd-20-1837. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 31.Barrett M, Boyne J, De Wit K, 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. doi: 10.1007/s13167-019-00188-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 32.Fan X, Yao Q, Li Y, 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. doi: 10.1109/JBHI.2018.2858789. [DOI] [PubMed] [Google Scholar]
  • 33.Kaplan A, Cao H, Kocks JWH, 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. doi: 10.1016/j.jaip.2021.02.014. [DOI] [PubMed] [Google Scholar]
  • 34.Colantonio S, Govoni L, Vitacca M, et al. Decision Making Concepts for the Remote, Personalized Evaluation of COPD Patients’ Health Status. Methods Inf Med. 2015;54(3):240–247. doi: 10.3414/ME13-02-0038. [DOI] [PubMed] [Google Scholar]
  • 35.Bugajski A, Lengerich A, Szalacha L, 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. doi: 10.1111/jnu.12618. [DOI] [PubMed] [Google Scholar]
  • 36.Tomita K, Nagao R, Tohda Y, et al. Deep learning facilitates the diagnosis of adult asthma. Allergol Int. 2019;68(4):456–461. doi: 10.1016/j.alit.2019.04.010. [DOI] [PubMed] [Google Scholar]
  • 37.Ather S, Kadir T, Gleeson F. Artificial intelligence and radiomics in pulmonary nodule management: current status and future applications. Clin Radiol. 2020;75(1):13–19. doi: 10.1016/j.crad.2019.04.017. [DOI] [PubMed] [Google Scholar]
  • 38.Porter P, Abeyratne U, Della P, 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. doi: 10.1186/s12931-019-1046-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 39.Yu G, Li Z, Li S, et al. The role of artificial intelligence in identifying asthma in pediatric inpatient setting. Ann Transl Med. 2020;8(21):1367. doi: 10.21037/atm-20-2501a. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 40.Pépin JL, Bailly S, Tamisier R, et al. Big Data in sleep apnoea: Opportunities and challenges. Respirology. 2020;25(5):486–494. doi: 10.1111/resp.13669. [DOI] [PubMed] [Google Scholar]
  • 41.Wu CT, Li GH, Chien JY, 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. doi: 10.2196/22591. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 42.Fernández-Caramés TM, Froiz-Míguez I, Blanco-Novoa O, 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. doi: 10.3390/s19153319. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 43.Hao Z, Cui S, Zhu Y, 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. doi: 10.1177/2040622320942415. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 44.Mendes-Soares H, Raveh-Sadka T, Cohen Y, 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. doi: 10.1001/jamanetworkopen.2018.8102. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 45.Rodriguez-León C, Villalonga C, Munoz-Torres M, et al. Mobile and Wearable Technology for the Monitoring of Diabetes-Related Parameters: Systematic Review. JMIR Mhealth Uhealth. 2021;9(6):e25138. doi: 10.2196/25138. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 46.Jourdan T, Debs N, Frindel C. 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. doi: 10.3390/s21144808. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 47.Hsu WC, Sugiarto T, Lin YJ, et al. Multiple-Wearable-Sensor-Based Gait Classification and Analysis in Patients with Neurological Disorders. Sensors (Basel) 2018;18(10):3397. doi: 10.3390/s18103397. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 48.Chomiak T, Xian W, Pei Z, 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. doi: 10.1007/s00702-019-02020-0. [DOI] [PubMed] [Google Scholar]
  • 49.Williamson JR, Telfer B, Mullany R, et al. Detecting Parkinson’s Disease from Wrist-Worn Accelerometry in the U.K. Biobank. Sensors (Basel) 2021;21(6):2047. doi: 10.3390/s21062047. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 50.Nam KH, Kim DH, Choi BK, et al. Internet of Things, Digital Biomarker, and Artificial Intelligence in Spine: Current and Future Perspectives. Neurospine. 2019;16(4):705–711. doi: 10.14245/ns.1938388.194. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 51.Merali ZG, Witiw CD, Badhiwala JH, et al. Using a machine learning approach to predict outcome after surgery for degenerative cervical myelopathy. PLoS One. 2019;14(4):e0215133. doi: 10.1371/journal.pone.0215133. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 52.Golabchi FN, Sapienza S, Severini G, 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. doi: 10.1186/s12891-018-2350-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 53.Araújo F, Nogueira MN, Silva J, 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. doi: 10.2196/25781. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 54.Chae SH, Kim Y, Lee KS, 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. doi: 10.2196/17216. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 55.Tropea P, Schlieter H, Sterpi I, et al. Rehabilitation, the Great Absentee of Virtual Coaching in Medical Care: Scoping Review. J Med Internet Res. 2019;21(10):e12805. doi: 10.2196/12805. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 56.Zhang H, Song C, Rathore AS, 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. doi: 10.1109/RBME.2020.2991813. [DOI] [PubMed] [Google Scholar]
  • 57.Zhang Y, Yu H, Dong R, et al. Application Prospect of Artificial Intelligence in Rehabilitation and Management of Myasthenia Gravis. Biomed Res Int. 2021;2021:5592472. doi: 10.1155/2021/5592472. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 58.Pareja-Galeano H, Garatachea N, Lucia A. Exercise as a Polypill for Chronic Diseases. Prog Mol Biol Transl Sci. 2015;135:497–526. doi: 10.1016/bs.pmbts.2015.07.019. [DOI] [PubMed] [Google Scholar]
  • 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.Tan TE, Anees A, Chen C, 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. doi: 10.1016/S2589-7500(21)00055-8. [DOI] [PubMed] [Google Scholar]

Articles from Current Medical Science are provided here courtesy of Nature Publishing Group

RESOURCES