{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T17:10:42Z","timestamp":1774631442944,"version":"3.50.1"},"reference-count":103,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2023,11,18]],"date-time":"2023-11-18T00:00:00Z","timestamp":1700265600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100006013","name":"United Arab Emirates (UAE) University","doi-asserted-by":"publisher","award":["G00003443"],"award-info":[{"award-number":["G00003443"]}],"id":[{"id":"10.13039\/501100006013","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Future Internet"],"abstract":"<jats:p>Edge AI, an interdisciplinary technology that enables distributed intelligence with edge devices, is quickly becoming a critical component in early health prediction. Edge AI encompasses data analytics and artificial intelligence (AI) using machine learning, deep learning, and federated learning models deployed and executed at the edge of the network, far from centralized data centers. AI enables the careful analysis of large datasets derived from multiple sources, including electronic health records, wearable devices, and demographic information, making it possible to identify intricate patterns and predict a person\u2019s future health. Federated learning, a novel approach in AI, further enhances this prediction by enabling collaborative training of AI models on distributed edge devices while maintaining privacy. Using edge computing, data can be processed and analyzed locally, reducing latency and enabling instant decision making. This article reviews the role of Edge AI in early health prediction and highlights its potential to improve public health. Topics covered include the use of AI algorithms for early detection of chronic diseases such as diabetes and cancer and the use of edge computing in wearable devices to detect the spread of infectious diseases. In addition to discussing the challenges and limitations of Edge AI in early health prediction, this article emphasizes future research directions to address these concerns and the integration with existing healthcare systems and explore the full potential of these technologies in improving public health.<\/jats:p>","DOI":"10.3390\/fi15110370","type":"journal-article","created":{"date-parts":[[2023,11,18]],"date-time":"2023-11-18T07:33:18Z","timestamp":1700292798000},"page":"370","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":81,"title":["Edge AI for Early Detection of Chronic Diseases and the Spread of Infectious Diseases: Opportunities, Challenges, and Future Directions"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9121-8766","authenticated-orcid":false,"given":"Elarbi","family":"Badidi","sequence":"first","affiliation":[{"name":"Department of Computer Science and Software Engineering, College of Information Technology, UAE University, Al-Ain P.O. Box 15551, United Arab Emirates"}]}],"member":"1968","published-online":{"date-parts":[[2023,11,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"719","DOI":"10.1038\/s41551-018-0305-z","article-title":"Artificial intelligence in healthcare","volume":"2","author":"Yu","year":"2018","journal-title":"Nat. Biomed. Eng."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"2328","DOI":"10.4103\/jfmpc.jfmpc_440_19","article-title":"Overview of artificial intelligence in medicine","volume":"8","author":"Malik","year":"2019","journal-title":"J. Family Med. Prim. Care"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Bohr, A., and Memarzadeh, K. (2020). Artificial Intelligence in Healthcare, Academic Press.","DOI":"10.1016\/B978-0-12-818438-7.00002-2"},{"key":"ref_4","first-page":"291","article-title":"Artificial Intelligence in Healthcare: Review and Prediction Case Studies","volume":"6","author":"Rong","year":"2020","journal-title":"Proc. Est. Acad. Sci. Eng."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"637","DOI":"10.1109\/JIOT.2016.2579198","article-title":"Edge Computing: Vision and Challenges","volume":"3","author":"Shi","year":"2016","journal-title":"IEEE Internet Things J."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1109\/MC.2017.9","article-title":"The Emergence of Edge Computing","volume":"50","author":"Satyanarayanan","year":"2017","journal-title":"Computer"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.jnca.2019.05.005","article-title":"Edge computing for Internet of Things: A survey, e-healthcare case study and future direction","volume":"140","author":"Ray","year":"2019","journal-title":"J. Netw. Comput. Appl."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"308","DOI":"10.1016\/j.ijinfomgt.2018.08.004","article-title":"Mobile edge computing based QoS optimization in medical healthcare applications","volume":"45","author":"Sodhro","year":"2019","journal-title":"Int. J. Inf. Manag."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"2950699","DOI":"10.1155\/2022\/2950699","article-title":"New Opportunities, Challenges, and Applications of Edge-AI for Connected Healthcare in Internet of Medical Things for Smart Cities","volume":"2022","author":"Kamruzzaman","year":"2022","journal-title":"J. Healthc. Eng."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"107524","DOI":"10.1016\/j.compeleceng.2021.107524","article-title":"An edge AI-enabled IoT healthcare monitoring system for smart cities","volume":"96","author":"Rathi","year":"2021","journal-title":"Comput. Electr. Eng."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"288","DOI":"10.1186\/s13054-021-03724-0","article-title":"Machine learning model for early prediction of acute kidney injury (AKI) in pediatric critical care","volume":"25","author":"Dong","year":"2021","journal-title":"Crit. Care"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1016\/j.cmpb.2019.06.010","article-title":"An intelligent warning model for early prediction of cardiac arrest in sepsis patients","volume":"178","author":"Sepehri","year":"2019","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"7796","DOI":"10.1039\/D0AN01484B","article-title":"Wearable sensors for continuous oral cavity and dietary monitoring toward personalized healthcare and digital medicine","volume":"145","author":"Hong","year":"2021","journal-title":"Analyst"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"14791641211058856","DOI":"10.1177\/14791641211058856","article-title":"Early detection of diabetic nephropathy in patient with type 2 diabetes mellitus: A review of the literature","volume":"18","author":"Thipsawat","year":"2021","journal-title":"Diab. Vasc. Dis. Res."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"385","DOI":"10.1016\/j.dsx.2020.04.017","article-title":"Prediction and early detection of cardiovascular disease in South Asians with diabetes mellitus","volume":"14","author":"Wander","year":"2020","journal-title":"Diabetes Metab. Syndr."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Schiffman, J.D., Fisher, P.G., and Gibbs, P. (2015). Early detection of cancer: Past, present, and future. Am. Soc. Clin. Oncol. Educ. Book, 57\u201365.","DOI":"10.14694\/EdBook_AM.2015.35.57"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Xu, L., Zhu, S., and Wen, N. (2022). Deep reinforcement learning and its applications in medical imaging and radiation therapy: A survey. Phys. Med. Biol., 67.","DOI":"10.1088\/1361-6560\/ac9cb3"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1186\/s13244-019-0832-5","article-title":"Deep learning workflow in radiology: A primer","volume":"11","author":"Montagnon","year":"2020","journal-title":"Insights Imaging"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1016\/j.gpb.2017.07.003","article-title":"Deep Learning and Its Applications in Biomedicine","volume":"16","author":"Cao","year":"2018","journal-title":"Genom. Proteom. Bioinform."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Moshawrab, M., Adda, M., Bouzouane, A., Ibrahim, H., and Raad, A. (2023). Reviewing Federated Machine Learning and Its Use in Diseases Prediction. Sensors, 23.","DOI":"10.3390\/s23042112"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Moshawrab, M., Adda, M., Bouzouane, A., Ibrahim, H., and Raad, A. (2023). Smart Wearables for the Detection of Cardiovascular Diseases: A Systematic Literature Review. Sensors, 23.","DOI":"10.3390\/s23020828"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1145\/2831347.2831354","article-title":"Edge-centric Computing: Vision and Challenges","volume":"45","author":"Montresor","year":"2015","journal-title":"SIGCOMM Comput. Commun. Rev."},{"key":"ref_23","unstructured":"Grand View Research (2023, February 16). Edge Computing Market Size, Share & Growth Report, 2023\u20132030. Available online: https:\/\/www.grandviewresearch.com\/industry-analysis\/edge-computing-market."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"17981","DOI":"10.1038\/s41598-022-22514-4","article-title":"Artificial intelligence-based methods for fusion of electronic health records and imaging data","volume":"12","author":"Mohsen","year":"2022","journal-title":"Sci. Rep."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"27","DOI":"10.3389\/fmed.2020.00027","article-title":"Artificial Intelligence in Medicine: Today and Tomorrow","volume":"7","author":"Briganti","year":"2020","journal-title":"Front. Med."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"2017","DOI":"10.1016\/j.drudis.2019.07.006","article-title":"Deep learning in drug discovery: Opportunities, challenges and future prospects","volume":"24","author":"Lavecchia","year":"2019","journal-title":"Drug Discov. Today"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"773","DOI":"10.1016\/j.drudis.2018.11.014","article-title":"Artificial intelligence in drug development: Present status and future prospects","volume":"24","author":"Mak","year":"2019","journal-title":"Drug Discov. Today"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"949","DOI":"10.1080\/17460441.2021.1909567","article-title":"Artificial intelligence in drug discovery: Recent advances and future perspectives","volume":"16","author":"Grisoni","year":"2021","journal-title":"Expert Opin. Drug Discov."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1038\/s41591-018-0300-7","article-title":"High-performance medicine: The convergence of human and artificial intelligence","volume":"25","author":"Topol","year":"2019","journal-title":"Nat. Med."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"6215281","DOI":"10.1155\/2021\/6215281","article-title":"Deep Learning Approach for Medical Image Analysis","volume":"2021","author":"Adegun","year":"2021","journal-title":"Comput. Intell. Neurosci."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Fraiwan, M., Audat, Z., Fraiwan, L., and Manasreh, T. (2022). Using deep transfer learning to detect scoliosis and spondylolisthesis from X-ray images. PLoS ONE, 17.","DOI":"10.1371\/journal.pone.0267851"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1837","DOI":"10.1109\/JBHI.2020.2991043","article-title":"AI in Medical Imaging Informatics: Current Challenges and Future Directions","volume":"24","author":"Panayides","year":"2020","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"101964","DOI":"10.1016\/j.artmed.2020.101964","article-title":"Reinforcement learning for intelligent healthcare applications: A survey","volume":"109","author":"Coronato","year":"2020","journal-title":"Artif. Intell. Med."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1205","DOI":"10.1007\/s10586-022-03717-w","article-title":"Edge computing based secure health monitoring framework for electronic healthcare system","volume":"26","author":"Singh","year":"2022","journal-title":"Clust. Comput."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"647","DOI":"10.1177\/1932296817717007","article-title":"Fog Computing and Edge Computing Architectures for Processing Data From Diabetes Devices Connected to the Medical Internet of Things","volume":"11","author":"Klonoff","year":"2017","journal-title":"J. Diabetes Sci. Technol."},{"key":"ref_36","unstructured":"American Diabetes Association (2023, February 18). What Is a Smart Insulin Pen?. Available online: https:\/\/diabetes.org\/about-diabetes\/devices-technology\/smart-insulin-pen."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1007\/s40138-022-00248-x","article-title":"The Use of Wearable ECG Devices in the Clinical Setting: A Review","volume":"10","author":"Kamga","year":"2022","journal-title":"Curr. Emerg. Hosp. Med. Rep."},{"key":"ref_38","unstructured":"Findair (2023, February 18). IoT with the Use of Smart Inhalers-Resources-Blog. Available online: http:\/\/findair.eu\/resources\/blog\/iot-with-the-use-of-smart-inhalers\/."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Dey, A., Haque, K.A., Nayan, A.A., and Kibria, M.G. (2020, January 28\u201329). IoT Based Smart Inhaler For Context-Aware Service Provisioning. Proceedings of the 2020 2nd International Conference on Advanced Information and Communication Technology (ICAICT), Dhaka, Bangladesh.","DOI":"10.1109\/ICAICT51780.2020.9333427"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"6483003","DOI":"10.1155\/2021\/6483003","article-title":"Edge Artificial Intelligence: Real-Time Noninvasive Technique for Vital Signs of Myocardial Infarction Recognition Using Jetson Nano","volume":"2021","author":"Mohan","year":"2021","journal-title":"Adv. Hum.-Comput. Interact."},{"key":"ref_41","first-page":"1191434","article-title":"Internet of Things- (IoT-) Based Real-Time Vital Physiological Parameter Monitoring System for Remote Asthma Patients","volume":"2022","author":"Islam","year":"2022","journal-title":"Proc. Int. Wirel. Commun. Mob. Comput. Conf."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1386470","DOI":"10.1155\/2018\/1386470","article-title":"Fog Computing-Based IoT for Health Monitoring System","volume":"2018","author":"Paul","year":"2018","journal-title":"J. Sens."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"108835","DOI":"10.1016\/j.asoc.2022.108835","article-title":"Intelligent monitoring for infectious diseases with fuzzy systems and edge computing: A survey","volume":"123","author":"Jiang","year":"2022","journal-title":"Appl. Soft Comput."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"6152041","DOI":"10.1155\/2020\/6152041","article-title":"Learning from Large-Scale Wearable Device Data for Predicting the Epidemic Trend of COVID-19","volume":"2020","author":"Zhu","year":"2020","journal-title":"Discrete Dyn. Nat. Soc."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"2826127","DOI":"10.1155\/2022\/2826127","article-title":"Identification and Prediction of Chronic Diseases Using Machine Learning Approach","volume":"2022","author":"Alanazi","year":"2022","journal-title":"J. Healthc. Eng."},{"key":"ref_46","unstructured":"Manjulatha, B., and Pabboju, S. (2021). Smart Computing Techniques and Applications, Springer."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Chhabra, D., Juneja, M., and Chutani, G. (2023). An efficient ensemble based machine learning approach for predicting Chronic Kidney Disease. Curr. Med. Imaging Rev., Online ahead of print.","DOI":"10.2174\/1573405620666230508104538"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1016\/j.breast.2019.12.007","article-title":"Artificial intelligence in digital breast pathology: Techniques and applications","volume":"49","author":"Ibrahim","year":"2020","journal-title":"Breast"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"e44248","DOI":"10.2196\/44248","article-title":"Artificial Intelligence for the Prediction and Early Diagnosis of Pancreatic Cancer: Scoping Review","volume":"25","author":"Jan","year":"2023","journal-title":"J. Med. Internet Res."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Huang, J.D., Wang, J., Ramsey, E., Leavey, G., Chico, T.J.A., and Condell, J. (2022). Applying Artificial Intelligence to Wearable Sensor Data to Diagnose and Predict Cardiovascular Disease: A Review. Sensors, 22.","DOI":"10.3390\/s22208002"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"235","DOI":"10.1097\/QCO.0000000000000935","article-title":"Machine learning and artificial intelligence for the diagnosis of infectious diseases in immunocompromised patients","volume":"36","author":"Tran","year":"2023","journal-title":"Curr. Opin. Infect. Dis."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"e941209-1","DOI":"10.12659\/MSM.941209","article-title":"Editorial: Infectious Disease Surveillance Using Artificial Intelligence (AI) and its Role in Epidemic and Pandemic Preparedness","volume":"29","author":"Parums","year":"2023","journal-title":"Med. Sci. Monit."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"2621655","DOI":"10.1155\/2021\/2621655","article-title":"A Machine-Learning-Based System for Prediction of Cardiovascular and Chronic Respiratory Diseases","volume":"2021","author":"Shah","year":"2021","journal-title":"J. Healthc. Eng."},{"key":"ref_54","first-page":"132","article-title":"Predictive analysis of heart diseases with machine learning approaches","volume":"2022","author":"Ramesh","year":"2022","journal-title":"Malays. J. Comput. Sci."},{"key":"ref_55","first-page":"3204","article-title":"Machine learning and artificial intelligence based Diabetes Mellitus detection and self-management: A systematic review","volume":"34","author":"Chaki","year":"2022","journal-title":"J. King Saud Univ. Comput. Inf. Sci."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1038\/s41591-021-01506-3","article-title":"Federated learning for predicting clinical outcomes in patients with COVID-19","volume":"27","author":"Dayan","year":"2021","journal-title":"Nat. Med."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"1","DOI":"10.3109\/10408363.2013.853025","article-title":"Risk predictive modelling for diabetes and cardiovascular disease","volume":"51","author":"Kengne","year":"2014","journal-title":"Crit. Rev. Clin. Lab. Sci."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1177\/0962280215626466","article-title":"A review of statistical updating methods for clinical prediction models","volume":"27","author":"Su","year":"2018","journal-title":"Stat. Methods Med. Res."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1037\/per0000300","article-title":"Health risk prediction models incorporating personality data: Motivation, challenges, and illustration","volume":"10","author":"Chapman","year":"2019","journal-title":"Personal. Disord."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"S116","DOI":"10.1111\/epi.16555","article-title":"Machine learning and wearable devices of the future","volume":"62","author":"Beniczky","year":"2021","journal-title":"Epilepsia"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"4653923","DOI":"10.1155\/2022\/4653923","article-title":"Machine Learning for Healthcare Wearable Devices: The Big Picture","volume":"2022","author":"Sabry","year":"2022","journal-title":"J. Healthc. Eng."},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Castelli Gattinara Di Zubiena, F., Menna, G., Mileti, I., Zampogna, A., Asci, F., Paoloni, M., Suppa, A., Del Prete, Z., and Palermo, E. (2022). Machine Learning and Wearable Sensors for the Early Detection of Balance Disorders in Parkinson\u2019s Disease. Sensors, 22.","DOI":"10.3390\/s22249903"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"100118","DOI":"10.1016\/j.health.2022.100118","article-title":"An assessment of machine learning models and algorithms for early prediction and diagnosis of diabetes using health indicators","volume":"2","author":"Chang","year":"2022","journal-title":"Healthc. Anal."},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Jenifer, A., Jeba, G., Paulraj, L., Kumar, N., Yuvaraj, T., Alen, G., Rozario, P., and Amoli, R. (2022, January 9\u201311). Edge-based heart disease prediction device using internet of things. Proceedings of the 2022 International Conference on Applied Artificial Intelligence and Computing (ICAAIC), Salem, India.","DOI":"10.1109\/ICAAIC53929.2022.9793104"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"853294","DOI":"10.3389\/fpubh.2022.853294","article-title":"Early-Stage Alzheimer\u2019s Disease Prediction Using Machine Learning Models","volume":"10","author":"Kavitha","year":"2022","journal-title":"Front. Public Health"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1016\/j.jbi.2015.05.016","article-title":"A comparison of models for predicting early hospital readmissions","volume":"56","author":"Futoma","year":"2015","journal-title":"J. Biomed. Inform."},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Guo, A., Mazumder, N.R., Ladner, D.P., and Foraker, R.E. (2021). Predicting mortality among patients with liver cirrhosis in electronic health records with machine learning. PLoS ONE, 16.","DOI":"10.1371\/journal.pone.0256428"},{"key":"ref_68","first-page":"533","article-title":"Learning to Identify Patients at Risk of Uncontrolled Hypertension Using Electronic Health Records Data","volume":"2019","author":"Mohammadi","year":"2019","journal-title":"AMIA Jt. Summits Transl. Sci. Proc."},{"key":"ref_69","unstructured":"(2021, January 19\u201320). Chronic Kidney Disease Prediction using Machine Learning Ensemble Algorithm. Proceedings of the 2021 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS), Greater Noida, India."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"9266889","DOI":"10.1155\/2023\/9266889","article-title":"Machine Learning Hybrid Model for the Prediction of Chronic Kidney Disease","volume":"2023","author":"Khalid","year":"2023","journal-title":"Comput. Intell. Neurosci."},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Toma\u0161ev, N., Glorot, X., Rae, J.W., Zielinski, M., Askham, H., Saraiva, A., Mottram, A., Meyer, C., Ravuri, S., and Protsyuk, I. (2019). Developing Deep Learning Continuous Risk Models for Early Adverse Event Prediction in Electronic Health Records: An AKI Case Study. Protoc. Exch.","DOI":"10.21203\/rs.2.10083\/v1"},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3501813","article-title":"Federated Learning for Healthcare: Systematic Review and Architecture Proposal","volume":"13","author":"Antunes","year":"2022","journal-title":"ACM Trans. Intell. Syst. Technol."},{"key":"ref_73","doi-asserted-by":"crossref","unstructured":"Nazir, S., and Kaleem, M. (2023). Federated Learning for Medical Image Analysis with Deep Neural Networks. Diagnostics, 13.","DOI":"10.3390\/diagnostics13091532"},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"28628","DOI":"10.1109\/ACCESS.2023.3260027","article-title":"A systematic review on federated learning in medical image analysis","volume":"11","author":"Sohan","year":"2023","journal-title":"IEEE Access"},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"778","DOI":"10.1109\/JBHI.2022.3181823","article-title":"Federated Learning for Privacy Preservation in Smart Healthcare Systems: A Comprehensive Survey","volume":"27","author":"Ali","year":"2022","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"72","DOI":"10.37737\/ace.22010","article-title":"Introduction to Clinical Prediction Models","volume":"4","author":"Iwagami","year":"2022","journal-title":"Ann. Clin. Epidemiol."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"357","DOI":"10.1038\/s41586-023-06160-y","article-title":"Health system-scale language models are all-purpose prediction engines","volume":"619","author":"Jiang","year":"2023","journal-title":"Nature"},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1016\/j.jpsychires.2023.05.014","article-title":"Early prediction of mental health problems following military deployment: Integrating pre- and post-deployment factors in neural network models","volume":"163","author":"Karstoft","year":"2023","journal-title":"J. Psychiatr. Res."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1038\/s41746-021-00482-9","article-title":"Predicting critical state after COVID-19 diagnosis: Model development using a large US electronic health record dataset","volume":"4","author":"Rinderknecht","year":"2021","journal-title":"NPJ Digit. Med."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"116","DOI":"10.1038\/s41586-019-1390-1","article-title":"A clinically applicable approach to continuous prediction of future acute kidney injury","volume":"572","author":"Glorot","year":"2019","journal-title":"Nature"},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3453476","article-title":"Federated Learning for Smart Healthcare: A Survey","volume":"55","author":"Nguyen","year":"2022","journal-title":"ACM Comput. Surv."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3339474","article-title":"Federated Machine Learning: Concept and Applications","volume":"10","author":"Yang","year":"2019","journal-title":"ACM Trans. Intell. Syst. Technol."},{"key":"ref_83","unstructured":"Beaussart, M., Grimberg, F., Hartley, M.A., and Jaggi, M. (2021). WAFFLE: Weighted averaging for personalized federated learning. arXiv."},{"key":"ref_84","doi-asserted-by":"crossref","unstructured":"Kalapaaking, A.P., Khalil, I., and Yi, X. (2023). Blockchain-based Federated Learning with SMPC Model Verification against Poisoning Attack for Healthcare Systems. arXiv.","DOI":"10.1109\/TETC.2023.3268186"},{"key":"ref_85","doi-asserted-by":"crossref","unstructured":"Randl, K., Armengol, N.L., Mondrejevski, L., and Miliou, I. (2023, January 22\u201324). Early prediction of the risk of ICU mortality with Deep Federated Learning. Proceedings of the 2023 IEEE 36th International Symposium on Computer-Based Medical Systems (CBMS), L\u2019Aquila, Italy.","DOI":"10.1109\/CBMS58004.2023.00304"},{"key":"ref_86","doi-asserted-by":"crossref","unstructured":"Yaqoob, M.M., Nazir, M., Khan, M.A., Qureshi, S., and Al-Rasheed, A. (2023). Hybrid Classifier-Based Federated Learning in Health Service Providers for Cardiovascular Disease Prediction. NATO Adv. Sci. Inst. Ser. E Appl. Sci., 13.","DOI":"10.3390\/app13031911"},{"key":"ref_87","unstructured":"Bharathi, K., Dhavamani, M., and Niranjan, K. (2022, January 29\u201331). A federated learning based approach for heart disease prediction. Proceedings of the 2022 6th International Conference on Computing Methodologies and Communication (ICCMC), Erode, India."},{"key":"ref_88","doi-asserted-by":"crossref","unstructured":"Nandhini, J.M., Joshi, S., and Anuratha, K. (2022, January 9\u201310). Federated learning based prediction of chronic kidney diseases. Proceedings of the 2022 1st International Conference on Computational Science and Technology (ICCST), Chennai, India.","DOI":"10.1109\/ICCST55948.2022.10040317"},{"key":"ref_89","doi-asserted-by":"crossref","unstructured":"Farooq, K., Syed, H.J., Alqahtani, S.O., Nagmeldin, W., Ibrahim, A.O., and Gani, A. (2022). Blockchain Federated Learning for In-Home Health Monitoring. Electronics, 12.","DOI":"10.3390\/electronics12010136"},{"key":"ref_90","doi-asserted-by":"crossref","unstructured":"Chen, B., Chen, T., Zeng, X., Zhang, W., Lu, Q., Hou, Z., Zhou, J., and Helal, S. (2023). DFML: Dynamic Federated Meta-Learning for Rare Disease Prediction. IEEE\/ACM Trans. Comput. Biol. Bioinform., Online ahead of print.","DOI":"10.1109\/TCBB.2023.3239848"},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"e24207","DOI":"10.2196\/24207","article-title":"Federated Learning of Electronic Health Records to Improve Mortality Prediction in Hospitalized Patients With COVID-19: Machine Learning Approach","volume":"9","author":"Vaid","year":"2021","journal-title":"JMIR Med. Inform."},{"key":"ref_92","doi-asserted-by":"crossref","unstructured":"Vaid, A., Jaladanki, S.K., Xu, J., Teng, S., Kumar, A., Lee, S., Somani, S., Paranjpe, I., De Freitas, J.K., and Wanyan, T. (2020). Federated Learning of Electronic Health Records Improves Mortality Prediction in Patients Hospitalized with COVID-19. medRxiv, Preprint.","DOI":"10.1101\/2020.08.11.20172809"},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"6656204","DOI":"10.1155\/2021\/6656204","article-title":"Research on Data Security and Privacy Protection of Wearable Equipment in Healthcare","volume":"2021","author":"Jiang","year":"2021","journal-title":"J. Healthc. Eng."},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1109\/TMSCS.2015.2498605","article-title":"Privacy and Security in Internet of Things and Wearable Devices","volume":"1","author":"Arias","year":"2015","journal-title":"IEEE Trans. Multi-Scale Comput. Syst."},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"21412","DOI":"10.1038\/s41598-022-25949-x","article-title":"Data quality evaluation in wearable monitoring","volume":"12","author":"Vieluf","year":"2022","journal-title":"Sci. Rep."},{"key":"ref_96","doi-asserted-by":"crossref","first-page":"14221","DOI":"10.1109\/JSEN.2020.3009368","article-title":"Impact of Wearable Measurement Properties and Data Quality on ADLs Classification Accuracy","volume":"21","author":"Poli","year":"2021","journal-title":"IEEE Sens. J."},{"key":"ref_97","doi-asserted-by":"crossref","unstructured":"Souza, J., Caballero, I., Vasco Santos, J., Lobo, M., Pinto, A., Viana, J., S\u00e1ez, C., Lopes, F., and Freitas, A. (2022). Multisource and temporal variability in Portuguese hospital administrative datasets: Data quality implications. J. Biomed. Inform., 136.","DOI":"10.1016\/j.jbi.2022.104242"},{"key":"ref_98","doi-asserted-by":"crossref","first-page":"109960","DOI":"10.1109\/ACCESS.2021.3102399","article-title":"A Comparative Performance Analysis of Data Resampling Methods on Imbalance Medical Data","volume":"9","author":"Khushi","year":"2021","journal-title":"IEEE Access"},{"key":"ref_99","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1109\/RBME.2022.3216531","article-title":"Systematic Review of Advanced AI Methods for Improving Healthcare Data Quality in Post COVID-19 Era","volume":"16","author":"Isgut","year":"2023","journal-title":"IEEE Rev. Biomed. Eng."},{"key":"ref_100","unstructured":"Vaughn, J., Baral, A., Vadari, M., and Boag, W. (2020, January 2\u20134). Dataset Bias in Diagnostic AI systems: Guidelines for Dataset Collection and Usage. Proceedings of the ACM Conference on Health, Inference and Learning, Toronto, ON, Canada."},{"key":"ref_101","doi-asserted-by":"crossref","first-page":"100702","DOI":"10.1016\/j.hlpt.2022.100702","article-title":"Addressing algorithmic bias and the perpetuation of health inequities: An AI bias aware framework","volume":"12","author":"Agarwal","year":"2023","journal-title":"Health Policy Technol."},{"key":"ref_102","doi-asserted-by":"crossref","unstructured":"Ennab, M., and Mcheick, H. (2022). Designing an Interpretability-Based Model to Explain the Artificial Intelligence Algorithms in Healthcare. Diagnostics, 12.","DOI":"10.3390\/diagnostics12071557"},{"key":"ref_103","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1109\/MITP.2015.2","article-title":"An Interoperability Solution for Legacy Healthcare Devices","volume":"17","author":"Lee","year":"2015","journal-title":"IT Prof."}],"container-title":["Future Internet"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-5903\/15\/11\/370\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T21:25:23Z","timestamp":1760131523000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-5903\/15\/11\/370"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,18]]},"references-count":103,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2023,11]]}},"alternative-id":["fi15110370"],"URL":"https:\/\/doi.org\/10.3390\/fi15110370","relation":{},"ISSN":["1999-5903"],"issn-type":[{"value":"1999-5903","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,11,18]]}}}