{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,17]],"date-time":"2026-06-17T09:54:54Z","timestamp":1781690094242,"version":"3.54.5"},"reference-count":45,"publisher":"Wiley","issue":"7","license":[{"start":{"date-parts":[[2026,6,11]],"date-time":"2026-06-11T00:00:00Z","timestamp":1781136000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,6,11]],"date-time":"2026-06-11T00:00:00Z","timestamp":1781136000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Expert Systems"],"published-print":{"date-parts":[[2026,7]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>Smart healthcare systems are expected to grow exponentially in the 6G era, relying on their capacity to provide superior network services in terms of data rates and deployment scale. This generates vast amounts of health data that must be processed in real\u2010time. Therefore, computational offloading for smart healthcare systems is an inevitable trend and is widely applied in numerous medical applications, ranging from image diagnosis and clinical treatment to responding to viral pandemics. In this study, we provide a comprehensive overview of computational offloading for Health Internet of Things (HIoT) applications, examining various aspects. Then, we propose a deep reinforcement learning (DRL)\u2010based intelligent task offloading framework that performs decision\u2010making on local devices, accounts for real\u2010time resource constraints and provides detailed analyses. The results demonstrate that the DRL\u2010based offloading strategy outperformed Greedy and Threshold\u2010based methods by 20%, 10% and 45% in latency, energy consumption and failure rate, respectively. Finally, we identified challenges and open issues related to pervasive smart healthcare systems.<\/jats:p>","DOI":"10.1111\/exsy.70330","type":"journal-article","created":{"date-parts":[[2026,6,12]],"date-time":"2026-06-12T06:02:16Z","timestamp":1781244136000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A\n                    <scp>DRL<\/scp>\n                    \u2010Based Offloading Approach for Smart Healthcare Systems"],"prefix":"10.1111","volume":"43","author":[{"given":"Nguyen Thi Thanh","family":"Hue","sequence":"first","affiliation":[{"name":"Faculty of Information Technology Hung Yen University of Technology and Education  Hungyen Vietnam"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4193-6062","authenticated-orcid":false,"given":"Abdellah","family":"Chehri","sequence":"additional","affiliation":[{"name":"Department of Mathematics and Computer Science 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