{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,24]],"date-time":"2026-04-24T03:18:29Z","timestamp":1777000709623,"version":"3.51.4"},"reference-count":66,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2021,9,6]],"date-time":"2021-09-06T00:00:00Z","timestamp":1630886400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["BDCC"],"abstract":"<jats:p>Artificial intelligence (AI) programs are applied to methods such as diagnostic procedures, treatment protocol development, patient monitoring, drug development, personalized medicine in healthcare, and outbreak predictions in global health, as in the case of the current COVID-19 pandemic. Machine learning (ML) is a field of AI that allows computers to learn and improve without being explicitly programmed. ML algorithms can also analyze large amounts of data called Big data through electronic health records for disease prevention and diagnosis. Wearable medical devices are used to continuously monitor an individual\u2019s health status and store it in cloud computing. In the context of a newly published study, the potential benefits of sophisticated data analytics and machine learning are discussed in this review. We have conducted a literature search in all the popular databases such as Web of Science, Scopus, MEDLINE\/PubMed and Google Scholar search engines. This paper describes the utilization of concepts underlying ML, big data, blockchain technology and their importance in medicine, healthcare, public health surveillance, case estimations in COVID-19 pandemic and other epidemics. The review also goes through the possible consequences and difficulties for medical practitioners and health technologists in designing futuristic models to improve the quality and well-being of human lives.<\/jats:p>","DOI":"10.3390\/bdcc5030041","type":"journal-article","created":{"date-parts":[[2021,9,6]],"date-time":"2021-09-06T10:59:12Z","timestamp":1630925952000},"page":"41","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":105,"title":["A Review of Artificial Intelligence, Big Data, and Blockchain Technology Applications in Medicine and Global Health"],"prefix":"10.3390","volume":"5","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2965-5348","authenticated-orcid":false,"given":"Supriya","family":"M.","sequence":"first","affiliation":[{"name":"Department of Information and Communication Engineering, Anna University, Chennai 600025, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9840-8335","authenticated-orcid":false,"given":"Vijay Kumar","family":"Chattu","sequence":"additional","affiliation":[{"name":"Department of Medicine, Temerty Faculty of Medicine, University of Toronto, Toronto, ON M5S 1A8, Canada"},{"name":"Division of Occupational Medicine, Occupational Medicine Clinic, St. Michael\u2019s Hospital, Toronto, ON M5C 2C5, Canada"},{"name":"Department of Public Health, Saveetha Medical College and Hospitals, Savitha Institute of Medical and Technical Sciences, Saveetha University, Chennai 600077, India"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,9,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s40537-019-0217-0","article-title":"Big data in healthcare: Management, analysis and future prospects","volume":"6","author":"Dash","year":"2019","journal-title":"J. Big Data"},{"key":"ref_2","unstructured":"Anirudh, V.K. (2020, December 24). What Is Machine Learning: Definition, Types, Applications and Examples. Available online: https:\/\/www.toolbox.com\/tech\/artificial-intelligence\/tech-101\/what-is-machine-learning-definition-types-applications-and-examples\/."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"59","DOI":"10.4258\/hir.2019.25.2.59","article-title":"Blockchain Technology and Healthcare","volume":"25","author":"Yoon","year":"2019","journal-title":"Health Inform. Res."},{"key":"ref_4","first-page":"of15017","article-title":"Blockchain in Healthcare: A Patient-Centered Model","volume":"20","author":"Chen","year":"2019","journal-title":"Biomed. J. Sci. Tech. Res."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"e17199","DOI":"10.2196\/17199","article-title":"Blockchain in Health Care: Hope or Hype?","volume":"22","author":"Clohessy","year":"2020","journal-title":"J. Med. Internet Res."},{"key":"ref_6","unstructured":"(2021, January 02). Flatworld Solutions. Top 10 Applications of Machine Learning in Healthcare. Available online: https:\/\/www.flatworldsolutions.com\/healthcare\/articles\/top-10-applications-of-machine-learning-in-healthcare.php."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"103290","DOI":"10.1016\/j.compind.2020.103290","article-title":"Blockchain in healthcare: A systematic literature review, synthesizing framework and future research agenda","volume":"122","author":"Tandon","year":"2020","journal-title":"Comput. Ind."},{"key":"ref_8","first-page":"154","article-title":"Block chain Technology in Healthcare","volume":"4","author":"Sadiku","year":"2018","journal-title":"Int. J. Adv. Sci. Res. Eng."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Cernian, A., Tiganoaia, B., Sacala, I., Pavel, A., and Iftemi, A. (2020). Patient Data Chain: A Block-chain-Based Approach to Integrate Personal Health Records. Sensors, 20.","DOI":"10.3390\/s20226538"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"265","DOI":"10.4258\/hir.2020.26.4.265","article-title":"Patient consent management by a purpose-based consent model for electronic health record based on blockchain technology","volume":"26","author":"Tith","year":"2020","journal-title":"Healthc. Inform. Res."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Benke, K., and Benke, G. (2018). Artificial intelligence and big data in public health. Int. J. Environ. Res. Public Health, 15.","DOI":"10.3390\/ijerph15122796"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1049","DOI":"10.1016\/j.procs.2018.05.020","article-title":"Big Data and Machine Learning Based Secure Healthcare Framework","volume":"132","author":"Kaur","year":"2018","journal-title":"Procedia Comput. Sci."},{"key":"ref_13","unstructured":"Milward, J. (2021, January 23). What Is Mobile Health?. Available online: https:\/\/www.addiction-ssa.org\/knowledge-hub\/what-is-mobile-health."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1155\/2020\/6654063","article-title":"Applications of Artificial Intelligence and Big Data Analytics in m-Health: A Healthcare System Perspective","volume":"2020","author":"Khan","year":"2020","journal-title":"J. Health Eng."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Ahad, M., Paiva, S., and Zafar, S. (2021). Recent Machine Learning and Internet of Things (IoT) Applications for Personalized Healthcare: Issues and Challenges. Sustainable and Energy Efficient Computing Paradigms for Society, EAI\/Springer Innovations in Communication and Computing.","DOI":"10.1007\/978-3-030-51070-1"},{"key":"ref_16","first-page":"1","article-title":"A new Internet of Things architecture for real-time prediction of various diseases using machine learning on big data environment","volume":"6","author":"Maalmi","year":"2019","journal-title":"J. Big Data"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Siddiqui, S., Nesbitt, R., Shakir, M.Z., Khan, A.A., Khan, A.A., Khan, K.K., and Ramzan, N. (2020). Artificial Neural Network (ANN) Enabled Internet of Things (IoT) Architecture for Music Therapy. Electronics, 9.","DOI":"10.3390\/electronics9122019"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Abdelaziz, A., Salama, A.S., Riad, A.M., and Mahmoud, A.N. (2019). A machine learning model for predicting of chronic kidney disease based internet of things and cloud computing in smart cities. Security in Smart Cities: Models, Applications, and Challenges, Springer.","DOI":"10.1007\/978-3-030-01560-2_5"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"106815","DOI":"10.1016\/j.measurement.2019.07.043","article-title":"Utilizing IoT wearable medical device for heart disease prediction using higher order Boltzmann model: A classification approach","volume":"147","author":"Almakhadmeh","year":"2019","journal-title":"Measurement"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"e3838","DOI":"10.1002\/ett.3838","article-title":"Innovative and efficient method of robotics for helping the Parkinson\u2019s disease patient using IoT in big data analytics","volume":"31","author":"Sivaparthipan","year":"2019","journal-title":"Trans. Emerg. Telecommun. Technol."},{"key":"ref_21","unstructured":"Grossfield\u2019s, B. (2021, January 30). Deep Learning VS. Machine Learning: A Simple Way to Learn the Difference. Available online: https:\/\/www.zendesk.com\/blog\/machine-learning-and-deep-learning."},{"key":"ref_22","unstructured":"(2021, July 31). What Is Heart Disease? Heart Disease (for Kids)\u2014Nemours Kidshealth. Available online: https:\/\/kidshealth.org\/en\/kids\/heart-disease.html."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1016\/j.tele.2018.11.007","article-title":"Identification of significant features and data mining techniques in predicting heart disease","volume":"36","author":"Amin","year":"2018","journal-title":"Telemat. Inform."},{"key":"ref_24","unstructured":"Dua, D., and Karra Taniskidou, E. (2017). UCI Machine Learning Repository. [Master\u2019s Thesis, School of Information and Computer Science, University of California]."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"81542","DOI":"10.1109\/ACCESS.2019.2923707","article-title":"Effective Heart Disease Prediction Using Hybrid Machine Learning Techniques","volume":"7","author":"Mohan","year":"2019","journal-title":"IEEE Access"},{"key":"ref_26","unstructured":"Archana, S., and Kumar, R. (2020, January 14\u201315). Heart disease prediction using machine learning algorithms. Proceedings of the 2020 International Conference on Electrical and Electronics Engineering (ICE3), Fargo, ND, USA."},{"key":"ref_27","first-page":"1","article-title":"A Hybrid Intelligent System Framework for the Prediction of Heart Disease Using Machine Learning Algorithms","volume":"2018","author":"Haq","year":"2018","journal-title":"Mob. Inf. Syst."},{"key":"ref_28","unstructured":"Saba, B., Khan, Z.S., Khan, F.H., Anjum, A., and Bashir, K. (2019, January 8\u201312). Improving heart disease prediction using feature selection approaches. Proceedings of the 2019 16th International Bhurban Conference on Applied Sciences and Technology (IBCAST), Islamabad, Pakistan."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41569-019-0294-y","article-title":"Integrating blockchain technology with artificial intelligence for cardiovascular medicine","volume":"17","author":"Krittanawong","year":"2019","journal-title":"Nat. Rev. Cardiol."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"220","DOI":"10.3389\/fnagi.2019.00220","article-title":"Deep learning in Alzheimer\u2019s disease: Diagnostic classification and prognostic prediction using neuroimaging data","volume":"11","author":"Jo","year":"2019","journal-title":"Front. Aging Neurosci."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"9","DOI":"10.3389\/fneur.2019.00009","article-title":"BHARAT: An Integrated Big Data Analytic Model for Early Diagnostic Biomarker of Alzheimer\u2019s Disease","volume":"10","author":"Sharma","year":"2019","journal-title":"Front. Neurol."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"777","DOI":"10.3389\/fnins.2018.00777","article-title":"Convolutional neural networks-based MRI image analysis for the Alzheimer\u2019s disease prediction from mild cognitive impairment","volume":"12","author":"Lin","year":"2018","journal-title":"Front. Neurosci."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"11050","DOI":"10.1073\/pnas.200033797","article-title":"Measuring the thickness of the human cerebral cortex from magnetic resonance images","volume":"97","author":"Fischl","year":"2000","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1093\/cercor\/bhg087","article-title":"Automatically Parcellating the Human Cerebral Cortex","volume":"14","author":"Fischl","year":"2004","journal-title":"Cereb. Cortex"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"968","DOI":"10.1016\/j.neuroimage.2006.01.021","article-title":"An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest","volume":"31","author":"Desikan","year":"2006","journal-title":"Neuroimage"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"180","DOI":"10.1016\/j.neuroimage.2006.02.051","article-title":"Reliability of MRI-derived measurements of human cerebral cortical thickness: The effects of field strength, scanner upgrade and manufacturer","volume":"32","author":"Han","year":"2006","journal-title":"NeuroImage"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"406","DOI":"10.3389\/fnagi.2018.00406","article-title":"Prediction of autopsy verified neuropathological change of Alzheimer\u2019s disease using machine learning and MRI","volume":"10","author":"Kautzky","year":"2018","journal-title":"Front. Aging Neurosci."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Almubark, I., Chang, L.-C., Nguyen, T., Turner, R.S., and Jiang, X. (2019, January 9\u201312). Early Detection of Alzheimer\u2019s Disease Using Patient Neuropsychological and Cognitive Data and Machine Learning Techniques. Proceedings of the 2019 IEEE International Conference on Big Data (Big Data), Los Angeles, CA, USA.","DOI":"10.1109\/BigData47090.2019.9006583"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Pilozzi, A., and Huang, X. (2020). Overcoming Alzheimer\u2019s Disease Stigma by Leveraging Artificial Intelligence and Blockchain Technologies. Brain Sci., 10.","DOI":"10.3390\/brainsci10030183"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"231","DOI":"10.1016\/j.fcij.2018.06.001","article-title":"Bio-inspired computing: Algorithms review, deep analysis, and the scope of applications","volume":"3","author":"Darwish","year":"2018","journal-title":"Futur. Comput. Inform. J."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Ghoniem, R.M. (2020). A Novel Bio-Inspired Deep Learning Approach for Liver Cancer Diagnosis. Information, 11.","DOI":"10.3390\/info11020080"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Elgin Christo, V.R., Khanna Nehemiah, H., Minu, B., and Kannan, A. (2019). Correlation-Based Ensemble Feature Se-lection Using Bioinspired Algorithms and Classification Using Backpropagation Neural Network. Comput. Math. Methods Med., 1\u201317.","DOI":"10.1155\/2019\/7398307"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"414","DOI":"10.1007\/s10729-019-09498-w","article-title":"A novel approach for breast cancer prediction using optimized ANN classifier based on big data environment","volume":"23","author":"Supriya","year":"2019","journal-title":"Heal. Care Manag. Sci."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"164","DOI":"10.1504\/IJICA.2019.103380","article-title":"Bio-inspired algorithms for diagnosis of breast cancer","volume":"10","author":"Sharma","year":"2019","journal-title":"Int. J. Innov. Comput. Appl."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"670","DOI":"10.1007\/s10729-019-09489-x","article-title":"IoT with cloud based lung cancer diagnosis model using optimal support vector machine","volume":"23","author":"Valluru","year":"2020","journal-title":"Health Care Manag. Sci."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Olivares, R., Munoz, R., Soto, R., Crawford, B., C\u00e1rdenas, D., Ponce, A., and Taramasco, C. (2020). An Optimized Brain-Based Algorithm for Classifying Parkinson\u2019s Disease. Appl. Sci., 10.","DOI":"10.3390\/app10051827"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"100","DOI":"10.1016\/j.cogsys.2018.12.002","article-title":"Diagnosis of Parkinson\u2019s disease using modified grey wolf optimization","volume":"54","author":"Sharma","year":"2018","journal-title":"Cogn. Syst. Res."},{"key":"ref_48","first-page":"11","article-title":"Parkinson\u2019s Disease Detection Using Biogeography-Based Optimization","volume":"61","author":"Hessam","year":"2019","journal-title":"Comput. Mater. Contin."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1007\/s13755-020-00104-w","article-title":"Bio-inspired dimensionality reduction for Parkinson\u2019s disease (PD) classification","volume":"8","author":"Pasha","year":"2020","journal-title":"Health Inf. Sci. Syst."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"138","DOI":"10.1504\/IJICA.2019.103370","article-title":"Parkinson\u2019s diagnosis using ant-lion optimisation algorithm","volume":"10","author":"Sharma","year":"2019","journal-title":"Int. J. Innov. Comput. Appl."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"2765","DOI":"10.1109\/JBHI.2020.3012487","article-title":"COVID-19 Optimizer Algorithm, Modeling and Controlling of Coronavirus Distribution Process","volume":"24","author":"Hosseini","year":"2020","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"79521","DOI":"10.1109\/ACCESS.2020.2990893","article-title":"A Hybrid COVID-19 Detection Model Using an Improved Marine Predators Algorithm and a Ranking-Based Diversity Reduction Strategy","volume":"8","author":"Mohamed","year":"2020","journal-title":"IEEE Access"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"110071","DOI":"10.1016\/j.chaos.2020.110071","article-title":"Recognition of COVID-19 disease from X-ray images by hybrid model consisting of 2D curvelet transform, chaotic salp swarm algorithm and deep learning technique","volume":"140","author":"Altan","year":"2020","journal-title":"Chaos Solitons Fractals"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Al-Qaness, M.A., Ewees, A.A., Fan, H., and Abd El Aziz, M. (2020). Optimization method for fore-casting confirmed cases of COVID-19 in China. J. Clin. Med., 9.","DOI":"10.3390\/jcm9030674"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"ELGhamrawy, S.M. (2020). Diagnosis and Prediction Model for COVID19 Patients Response to Treatment based on Convolutional Neural Networks and Whale Optimization Algorithm Using CT Images. MedRxiv, 1\u201323.","DOI":"10.1101\/2020.04.16.20063990"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"101812","DOI":"10.1016\/j.compmedimag.2020.101812","article-title":"An Integration of blockchain and AI for secure data sharing and detection of CT images for the hospitals","volume":"87","author":"Kumar","year":"2020","journal-title":"Comput. Med. Imaging Graph."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"702","DOI":"10.1109\/TEVC.2008.919004","article-title":"Biogeography-based optimization","volume":"12","author":"Simon","year":"2008","journal-title":"IEEE Trans. Evol. Comput."},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Yang, X.S. (2012, January 3\u20137). Flower pollination algorithm for global optimization. Proceedings of the International Conference on Unconventional Computing and Natural Computation, Orl\u00e9ans, France.","DOI":"10.1007\/978-3-642-32894-7_27"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1016\/j.advengsoft.2017.07.002","article-title":"Salp Swarm Algorithm: A bio-inspired optimizer for engineering design problems","volume":"114","author":"Mirjalili","year":"2017","journal-title":"Adv. Eng. Softw."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1016\/j.advengsoft.2016.01.008","article-title":"The whale optimization algorithm","volume":"95","author":"Mirjalili","year":"2016","journal-title":"Adv. Eng. Softw."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Fusco, A., Dicuonzo, G., Dell\u2019Atti, V., and Tatullo, M. (2020). Blockchain in healthcare: Insights on COVID-19. Int. J. Environ. Res. Public Health, 17.","DOI":"10.3390\/ijerph17197167"},{"key":"ref_62","first-page":"199","article-title":"Emerging technologies in healthcare: A tutorial","volume":"5","author":"Sadiku","year":"2019","journal-title":"Int. J. Adv. Sci. Res. Eng. (IJASRE)"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1016\/S1386-5056(02)00064-3","article-title":"Combining diagnosis and treatment using ASBRU","volume":"68","author":"Seyfang","year":"2002","journal-title":"Int. J. Med. Inform."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"331","DOI":"10.1080\/17434440.2020.1741348","article-title":"Theranostic approaches in nuclear medicine: Current status and future prospects","volume":"17","author":"Filippi","year":"2020","journal-title":"Expert Rev. Med. Devices"},{"key":"ref_65","unstructured":"NEJM Catalyst (2021, July 31). What is Value-Based healthcare?. Available online: https:\/\/catalyst.nejm.org\/doi\/full\/10.1056\/CAT.17.0558."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"1032","DOI":"10.1007\/s11606-018-4818-7","article-title":"Core functions and forms of complex health interventions: A patient-centered medical home illustration","volume":"34","author":"Jolles","year":"2019","journal-title":"J. Gen. Intern. Med."}],"container-title":["Big Data and Cognitive Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2504-2289\/5\/3\/41\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:56:59Z","timestamp":1760165819000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2504-2289\/5\/3\/41"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,9,6]]},"references-count":66,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2021,9]]}},"alternative-id":["bdcc5030041"],"URL":"https:\/\/doi.org\/10.3390\/bdcc5030041","relation":{},"ISSN":["2504-2289"],"issn-type":[{"value":"2504-2289","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,9,6]]}}}