# IoMT–Fog–Cloud-based AI frameworks for chronic disease diagnosis: updated comparative analysis with recent AI-IoMT models (2020–2025)

## TL;DR

This paper reviews AI frameworks combining IoMT, Fog, and Cloud computing for diagnosing chronic diseases like diabetes and cardiovascular disease, comparing performance and system efficiency.

## Contribution

The paper provides an updated comparative analysis of AI-IoMT models from 2020–2025, emphasizing both diagnostic accuracy and network quality-of-service.

## Key findings

- Diabetes-focused AI models report accuracy around 95%–96%, while some cardiovascular models show >99% accuracy in controlled settings.
- Placing preprocessing and inference at the Fog layer reduces latency but may increase energy consumption with multi-Fog setups.
- The paper organizes 14 metrics into diagnostic performance and system/network QoS for reproducible comparisons and design recommendations.

## 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.