## 4 Problem Space for DL Applications
### IoMT Architecture Layers
#### 4.1.2 Network Layer  
Adopting IoMT systems requires a large volume of medical data to be passed through network systems, including high-resolution medical images, real-time monitoring data, and \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Category** & **Home** & **Wearables (Including Sensor Information)** & **Hospital** \\ \hline \multirow{8}{*}{Precision Augmentation} & [PERSON]’s disease evaluation [118]. & Atrial fibrillation early detection with smartwatches [119], or device STAT-ON. The sensor layer includes sensors such as accelerometers to get position on the body [118]. & Detection of atrial fibrillation [120]. \\ \cline{2-3}  & Heart disease remote monitoring system [121]. & Heart monitoring [122], The sensor layer includes sensors such as blood pressure, oxygen, and heart rate [121]. & Surgical robotic systems [123; 124; 125]. \\ \cline{2-3}  & Stress-level detection [126; 127]. & Three stress tests were conducted with a wearable system that relies on electrocardiogram (ECG), respiration, skin conductance, as well as surface electrocardiogram (sEMG) [126]. & Fuzzy logic DL in iStree application [126]. \\ \cline{2-3}  & & Stress-track-sensing wrist band with sensors to measure temperature, humidity, and steps [127]. & Trained a meta-model on stress prediction values to improve the model’s performance [127]. \\ \hline \multirow{4}{*}{Risk Assessment} & Healthcare Monitoring System (i-NXGeVita) [128]. & ECG sensor (module ad8232) collects heartbeats (heart signals) of the patient [128]. & i-NXGeVita (TensorFlow + GPU to train the model) [128]. \\ \cline{2-3}  & & Risk factor monitoring such as stroke detection with sensors to test oxygen [129], ECG [128], etc. & Smart surgery [130], smart laparoscopic forces in minimally invasive surgery system [131]. \\ \hline \multirow{4}{*}{Personalized Treatment} & Stress recognition in smart home [126; 127; 132]. & Biomedical systems for intelligent healthcare [126; 126]. & Health monitoring in diabetics [133], or fuzzy logic DL in iStree application [126]. \\ \cline{2-3}  & Prediction of sleep stages [134]. & Self-management of cardiovascular conditions [135]. & Wheelchair control enhancement [123]. \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Related works in sensor layer. patient records. Although current network systems have significantly improved bandwidth, the exponential growth in IoMT devices and the associated data creates potential risks such as data overflow, network congestion, and latency. These challenges could compromise the reliability, accuracy, and timeliness of medical diagnostics and treatment delivery [136]. DL can be leveraged to optimize the performance of IoMT systems by reducing the data load on network infrastructures while maintaining the accuracy required for medical decision making. For instance, federated learning allows processing and analysis of medical data--such as for images, sensor readings, or real-time monitoring streams locally or at the edge. DL models can extract essential features, compress data, or perform preliminary diagnostic assessments before sending data to central servers. These methods minimize the amount of raw data transmitted through the network, ensuring efficient bandwidth utilization while preserving diagnostic accuracy to enable healthcare professionals to make reliable and informed decisions. The network layer is responsible for bridging the perception layer (sensing devices) and the AI-powered decision-making layer. Data from wearable and implantable IoMT devices are transmitted securely via wireless communication protocols, processed at edge nodes for immediate insights, and then sent to cloud-based platforms for further AI analysis. Healthcare providers receive real-time notifications and alerts, allowing them to intervene proactively, manage chronic diseases efficiently, and reduce hospital readmissions.