IoMT–Fog–Cloud-based AI frameworks for chronic disease diagnosis: updated comparative analysis with recent AI-IoMT models (2020–2025)
| dc.contributor.author | Locharoenrat, Kitsakorn | |
| dc.date.accessioned | 2026-08-06T10:53:46Z | |
| dc.date.available | 2026-08-06T10:53:46Z | |
| dc.date.issued | 2026-01-01 | |
| dc.description.abstract | Chronic diseases such as diabetes and cardiovascular disease require frequent monitoring and timely clinical feedback to prevent complications. Internet of Medical Things (IoMT) systems increasingly combine near-patient sensing with Fog and Cloud computing so that time-critical preprocessing and inference can run close to the patient while compute-intensive training and population-level analytics remain in the Cloud. This review synthesizes primary studies published between 2020 and 2025 that implement AI-enabled IoMT, with an emphasis on systems that report both diagnostic performance and network quality-of-service (QoS). Following PRISMA 2020, we screened database records and included 14 primary studies; we focus the joint performance–QoS synthesis on six IoMT–Fog–Cloud frameworks for diabetes and cardiovascular disease and compare them with two recent multi-disease AI-IoMT models (DACL and TasLA). Diabetes-oriented implementations commonly report accuracy around 95%–96% using explainable or ensemble deep learning, whereas some cardiovascular frameworks report >99% accuracy in controlled settings; we therefore discuss plausible sources of optimistic performance, including small datasets, class imbalance, curated benchmarks, and potential leakage/overfitting in simulation-based evaluations. Across IoMT–Fog–Cloud studies, placing preprocessing and/or inference at the Fog layer repeatedly reduces end-to-end latency for streaming biosignals, but multi-Fog provisioning can increase energy and power demands. To support more reproducible comparisons, we organize 14 extracted metrics into (i) diagnostic performance (accuracy, precision, recall, F1-score, sensitivity, specificity) and (ii) system/network QoS (latency, jitter, throughput, bandwidth utilization, processing/execution time, network usage, energy consumption, power consumption), and we translate the evidence into study-linked design recommendations for future deployments. | |
| dc.identifier.citation | Frontiers in Medical Technology, 8, 2026 | |
| dc.identifier.doi | 10.3389/fmedt.2026.1748964 | |
| dc.identifier.issn | 26733129 | |
| dc.identifier.other | 2-s2.0-105038330596 | |
| dc.identifier.uri | https://dspace.kmitl.ac.th/handle/123456789/17645 | |
| dc.source | Frontiers in Medical Technology | |
| dc.subject | chronic disease diagnosis | |
| dc.subject | cloud computing | |
| dc.subject | deep learning | |
| dc.subject | fog computing | |
| dc.subject | internet of Medical Things (IoMT) | |
| dc.subject | network performance | |
| dc.title | IoMT–Fog–Cloud-based AI frameworks for chronic disease diagnosis: updated comparative analysis with recent AI-IoMT models (2020–2025) | |
| dc.type | Review |