[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117198-en":3,"doc-seo-117198-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},117198,5909877438554,"Maeve","https://ap-avatar.wpscdn.com/avatar/5600025385ad2bf12a7?_k=1778553567797529272",7,"Healthcare","Enhancing medical services through machine learning and UAV technology: Applications and benefits","This chapter examines how integrating unmanned aerial vehicles (UAVs) with machine learning can strengthen medical services across multiple operational scenarios. It describes UAV-enabled automated delivery of medical supplies to remote or inaccessible areas, improving timely access to critical items. It also covers UAV-assisted remote patient monitoring with real-time data collection and analysis for early health issue detection. The discussion further extends to emergency response support, disease surveillance, and outbreak management through hotspot identification and illness spread prediction.","Documents  \nSaeed, R.A.a , Saeed, M. M. b , Ahmed, [Z.E.](Z.E.c d)[c d](Z.E.c d) , Hashim, A. H.A.d  \nEnhancing medical services through machine learning and UAV technology: Applications and benefits  \n(2024) Applications of Machine Learning in UAV Networks , pp. 307-343.  \nDOI: [10.4018/979-8-3693-0578-2.ch012](10.4018/979-8-3693-0578-2.ch012)  \na Department of Computer Engineering, College of Computers and Information Technology, Taif University, Saudi Arabia b Department of Communications and Electronics Engineering, Faculty of Engineering, University of Modern Sciences, Yemen  \nc Department of Computer Engineering, University of Gezira, Sudan  \nd Department of Electrical and Computer Engineering, International Islamic University Malaysia, Malaysia  \nAbstract  \nThis chapter focuses on the enhancement of medical services through the integration of unmanned aerial vehicle (UAV) technology and machine learning algorithms. It explores the broad spectrum of applications and benefits that arise from combining these two technologies. By employing UAVs for automated delivery, medical supplies can be efficiently transported to remote or inaccessible regions, thereby improving access to vital items. Remote patient monitoring, facilitated through UAVs and machine learning, enables real-time data collection and analysis, enabling the early identification of health issues. UAVs equipped with medical equipment and machine learning capabilities enhance emergency medical response by providing immediate assistance during critical situations. Disease surveillance and outbreak management can benefit from the use of UAVs and machine-learning algorithms to identify disease hotspots and predict the spread of illnesses. © 2024, IGI Global. All rights reserved.  \nReferences  \n Abdelhaleem, R.S. , Omri, M. , Abdel-Khalek, S.  \nOptimal path planning for drones based on swarm intelligence algorithm  \n(2022) Neural Comput & Applic,  \n Abir, S.A.A. , Islam, S.N. , Anwar, A. , Mahmood, A.N. , Oo, A.M.T.  \nBuilding resilience against COVID-19 pandemic using artificial intelligence, machine learning, and IoT: A survey of recent progress  \n(2020) IoT, 1 (2), pp. 506-528.  \n Ahmad, A. , Ordoñez, J. , Cartujo, P. , Martos, V.  \nRemotely piloted aircraft (RPA) in agriculture: A pursuit of sustainability  \n(2020) Agronomy (Basel) , 11 (1), p. 7.  \n Aiken, L.H. , Sermeus, W. , den Heede, V. , Sloane, D.M. , Busse, R. , McKee, M. , Bruyneel, L. , Kutney-Lee, A.  \nPatient safety, satisfaction, and quality of hospital care: Cross sectional surveys of nurses and patients in 12 countries in Europe and the United States  \n(2012) BMJ (Clinical Research Ed.) , 344 (202), p. 344.  \nPMID:22434089  \n Al-Wathinani, A.M. , Alhallaf, M.A. , Borowska-Stefańska, M. , Wiśniewski, S. , Sultan,  \nM.A.S. , Samman, O.Y. , Goniewicz, K.  \nElevating Healthcare: Rapid Literature Review on Drone Applications for  \nStreamlining Disaster Management and Prehospital Care in Saudi Arabia. [) . MDPI.]  \n(2023) Health Care , 11 (11), p. 1575.  \nPMID:37297715  \n Alatabani, L.E. , Ali, E.S. , Saeed, R.A.  \nDeep Learning Approaches for IoV Applications and Services  \n(2021) Intelligent Technologies for Internet of Vehicles. Internet of Things (Technology,  \nCommunications, and Computing) ,  \nN. Magaia, G. Mastorakis, C. Mavromoustakis, E. Pallis, & E. K. Markakis (Eds. ), Springer  Ali, E.H.A. , Zahraa, T.M. , Hassan, M.B. , Saeed, R.  \nAlgorithms Optimization for Intelligent IoV Applications  \n(2021) Handbook of Research on Innovations and Applications of AI, IoT, and Cognitive Technologies, pp. 1-25.  \nJ. Zhao & V. Vinoth Kumar (Eds. ), IGI Global  \n Almousa, O. , Zhang, R. , Dimma, M. , Yao, J. , Allen, A. , Chen, L. , Heidari, P. , Qayumi, K.  \nVirtual reality technology and remote digital application for tele-simulation and global medical education: An innovative hybrid system for clinical training  \n(2021) Simulation & Gaming, 52 (5), pp. 614-634.  \n Alqurashi, F.A. , Alsolami, F., Abdel-K","cbCaimnBzsL9vAKT","https://ap.wps.com/l/cbCaimnBzsL9vAKT","pdf",370658,1,13,"English","en",105,"# Applications of Machine Learning in UAV Networks\n## Automated delivery of medical supplies\n## Remote patient monitoring and early detection\n## Emergency medical response support\n## Disease surveillance and outbreak management","[{\"question\":\"How do UAVs improve access to medical supplies in remote areas?\",\"answer\":\"UAVs enable automated delivery of medical supplies, transporting them efficiently to remote or inaccessible regions and improving access to vital items.\"},{\"question\":\"What role does machine learning play in remote patient monitoring?\",\"answer\":\"Machine learning combined with UAVs supports real-time data collection and analysis, enabling earlier identification of health issues.\"},{\"question\":\"How can UAVs and machine learning support disease surveillance and outbreak management?\",\"answer\":\"They can help identify disease hotspots and predict the spread of illnesses using UAV-collected and analyzed information.\"}]","Enhancing medical services through machine learning and UAV technology: Applications and benefits | 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