{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T14:21:46Z","timestamp":1785421306697,"version":"3.56.0"},"reference-count":39,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2025,8,21]],"date-time":"2025-08-21T00:00:00Z","timestamp":1755734400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"King Salman Center For Disability Research"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Background: By leveraging advanced wireless technologies, Healthcare Industry 5.0 promotes the continuous monitoring of real-time medical acquisition from the physical environment. These systems help identify early diseases by collecting health records from patients\u2019 bodies promptly using biosensors. The dynamic nature of medical devices not only enhances the data analysis in medical services and the prediction of chronic diseases, but also improves remote diagnostics with the latency-aware healthcare system. However, due to scalability and reliability limitations in data processing, most existing healthcare systems pose research challenges in the timely detection of personalized diseases, leading to inconsistent diagnoses, particularly when continuous monitoring is crucial. Methods: This work propose an adaptive and secure framework for disability identification using the Internet of Medical Things (IoMT), integrating edge computing and artificial intelligence. To achieve the shortest response time for medical decisions, the proposed framework explores lightweight edge computing processes that collect physiological and behavioral data using biosensors. Furthermore, it offers a trusted mechanism using decentralized strategies to protect big data analytics from malicious activities and increase authentic access to sensitive medical data. Lastly, it provides personalized healthcare interventions while monitoring healthcare applications using realistic health records, thereby enhancing the system\u2019s ability to identify diseases associated with chronic conditions. Results: The proposed framework is tested using simulations, and the results indicate the high accuracy of the healthcare system in detecting disabilities at the edges, while enhancing the prompt response of the cloud server and guaranteeing the security of medical data through lightweight encryption methods and federated learning techniques. Conclusions: The proposed framework offers a secure and efficient solution for identifying disabilities in healthcare systems by leveraging IoMT, edge computing, and AI. It addresses critical challenges in real-time disease monitoring, enhancing diagnostic accuracy and ensuring the protection of sensitive medical data.<\/jats:p>","DOI":"10.3390\/diagnostics15162104","type":"journal-article","created":{"date-parts":[[2025,8,21]],"date-time":"2025-08-21T08:40:48Z","timestamp":1755765648000},"page":"2104","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["AI-Powered Adaptive Disability Prediction and Healthcare Analytics Using Smart Technologies"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3484-0994","authenticated-orcid":false,"given":"Malak","family":"Alamri","sequence":"first","affiliation":[{"name":"Department of Computer Science, College of Computer and Information Sciences, Jouf University, Sakaka 72311, Saudi Arabia"},{"name":"Research and Innovation Groups, King Salman Center for Disability Research, Riyadh 11614, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6339-2257","authenticated-orcid":false,"given":"Mamoona","family":"Humayun","sequence":"additional","affiliation":[{"name":"Department of Computing, School of Arts Humanities and Social Sciences, University of Roehampton, London SW15 5PH, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6657-9308","authenticated-orcid":false,"given":"Khalid","family":"Haseeb","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Islamia College Peshawar, Peshawar 25120, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Naveed","family":"Abbas","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Islamia College Peshawar, Peshawar 25120, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5088-1462","authenticated-orcid":false,"given":"Naeem","family":"Ramzan","sequence":"additional","affiliation":[{"name":"School of Computing, Engineering and Physical Sciences, University of West of Scotland, Paisley PA1 2BE, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,8,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"6622","DOI":"10.1007\/s11227-023-05649-7","article-title":"Agent-based blockchain model for robust authentication and authorization in IoT-based healthcare systems","volume":"80","author":"Idrissi","year":"2024","journal-title":"J. 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