Table 1.

Summary of existing studies highlighting key techniques, methodologies, datasets, and performance outcomes across related research.

Reference Number	Objective	Method	Dataset	Measures
Babar et al.11	To develop a secure, intelligent, and efficient SDN-driven DL framework	SDN, BiLSTM, DL, CNN	CICIDS2019 Dataset	Accuracy, Precision, Recall, F1-Score, TPR, FNR
Lakshmanaprakash et al.12	To develop an IoMT- enabled DON model for efficient disease diagnosis and real- time health monitoring	COFS, CRAN, CFO	Open-Source Medical Datasets	Accuracy, Efficiency, Time Complexity
Jiang et al.13	To develop a privacy-preserving and accurate EEG- based emotion recognition system using FL	TCN, LSTM, GRU, Gompertz Fuzzy Rank, Asynchronous Dropout Algorithm	GAMEEMO, SEED, DEAP	Accuracy, F1-Score
Baseer et al.14	To develop a predictive model for coronary artery disease using AI and IoMT	IoMT-based Data Acquisition, TabNet, CatBoost, Real-Time Data Processing	Diverse Patient Profiles	Accuracy, Interpretability, Prediction Speed
Ramani et al.15	To develop an optimized and secure data management framework	ODMSM-FL, BC- based Security, Decentralized Architecture	IoMT-Generated Medical Data	Accuracy, Latency, Security, Throughput, Convergence Rate
Ali et al.16	The objective is to develop a secure and efficient hybrid DL model	Hybrid DL, Homomorphic Encryption, Consortium BC, Edge-Cloud Collaboration	Encrypted EMR Data	Security, Efficiency, Latency
Ashok and Gopikrishnan17	To develop a dynamically reconfigurable hybrid metaheuristic BC model	EHO, GWO, BC, Dual Fitness Functions	Simulated IoMT Attack Scenarios	Attack Detection Speed, Throughput, Packet Delivery Ratio
Shukla et al.18	To develop a hybrid hierarchical DL with optimization models	SQCOA, OLSTM, HCSNN	IoMT sensor data	Accuracy, Prediction Performance