Artificial Intelligence-Enabled Medical Diagnosis in the Internet of Medical Things: An End-to-End Survey of Connected Sensing, Distributed Intelligence, Clinical Applications, and Validation.
- 1Electrical and Electronics Engineering Department, Karabük University, 78050 Karabük, Türkiye.
- 2Department of Electrical and Electronics Engineering, Faculty of Engineering, Gaziantep University, 27310 Gaziantep, Türkiye.
- 3Department of Drone Techniques Engineering, Technical College of Mosul, Northern Technical University, Mosul 41001, Iraq.
Abstract
The Internet of Medical Things (IoMT) can extend diagnostic observation beyond episodic encounters by connecting wearable, point-of-care, imaging, and bedside devices to artificial-intelligence services. Yet continuous measurement is not itself diagnosis: a clinically useful system must establish signal validity, map an output to a defined diagnostic role, communicate uncertainty, and demonstrate agreement with an appropriate reference standard. This survey analyzes the complete IoMT diagnostic pathway from acquisition and quality control through edge-fog-cloud computation, machine and deep learning, federated training, and clinician-governed action. It distinguishes monitoring, screening, early detection, differential diagnosis, prognosis, and decision support because these tasks require different labels, thresholds, metrics, and evidence. Evidence is synthesized across electrocardiography and cardiovascular risk, glucose and metabolic assessment, connected imaging and oncology, respiratory and infectious-disease detection, electroencephalography and seizure recognition, and decentralized point-of-care testing. The analysis shows how sensor placement, missingness, compression, latency, privacy mechanisms, and site heterogeneity can change diagnostic performance even when the model is unchanged. A diagnostic evidence framework is therefore proposed that links reference standards and patient-level data separation to sensitivity, specificity, calibration, external validation, target-device feasibility, uncertainty-based referral, and prospective workflow utility. Security, explainability, and lifecycle monitoring are treated as conditions of diagnostic reliability rather than independent features. The resulting synthesis provides a clinically centered basis for deciding which IoMT outputs may support screening or diagnosis, which remain monitoring signals, and what evidence is still required before deployment.
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BibTeX
@article{aziz2026artificial,
title = {{Artificial Intelligence-Enabled Medical Diagnosis in the Internet of Medical Things: An End-to-End Survey of Connected Sensing, Distributed Intelligence, Clinical Applications, and Validation.}},
author = {Ahlem Aziz and Seydi Kaçmaz and Azam Isam Abdulkareem Aladwani},
journal = {Diagnostics (Basel, Switzerland)},
year = {2026},
month = sep,
doi = {10.3390/diagnostics16193171},
url = {https://europepmc.org/article/MED/42882326},
note = {CC BY},
}
RIS
TY - JOUR
TI - Artificial Intelligence-Enabled Medical Diagnosis in the Internet of Medical Things: An End-to-End Survey of Connected Sensing, Distributed Intelligence, Clinical Applications, and Validation.
AU - Ahlem Aziz
AU - Seydi Kaçmaz
AU - Azam Isam Abdulkareem Aladwani
PY - 2026
DA - 2026/09/29
JO - Diagnostics (Basel, Switzerland)
DO - 10.3390/diagnostics16193171
UR - https://europepmc.org/article/MED/42882326
AB - The Internet of Medical Things (IoMT) can extend diagnostic observation beyond episodic encounters by connecting wearable, point-of-care, imaging, and bedside devices to artificial-intelligence services. Yet continuous measurement is not itself diagnosis: a clinically useful system must establish signal validity, map an output to a defined diagnostic role, communicate uncertainty, and demonstrate agreement with an appropriate reference standard. This survey analyzes the complete IoMT diagnostic pathway from acquisition and quality control through edge-fog-cloud computation, machine and deep learning, federated training, and clinician-governed action. It distinguishes monitoring, screening, early detection, differential diagnosis, prognosis, and decision support because these tasks require different labels, thresholds, metrics, and evidence. Evidence is synthesized across electrocardiography and cardiovascular risk, glucose and metabolic assessment, connected imaging and oncology, respiratory and infectious-disease detection, electroencephalography and seizure recognition, and decentralized point-of-care testing. The analysis shows how sensor placement, missingness, compression, latency, privacy mechanisms, and site heterogeneity can change diagnostic performance even when the model is unchanged. A diagnostic evidence framework is therefore proposed that links reference standards and patient-level data separation to sensitivity, specificity, calibration, external validation, target-device feasibility, uncertainty-based referral, and prospective workflow utility. Security, explainability, and lifecycle monitoring are treated as conditions of diagnostic reliability rather than independent features. The resulting synthesis provides a clinically centered basis for deciding which IoMT outputs may support screening or diagnosis, which remain monitoring signals, and what evidence is still required before deployment.
SN - 2075-4418
ER -