## 5 Challenges
### Scalability  
As healthcare systems incorporate an increasing number of devices and data sources, ensuring scalability and maintaining system performance has become a critical challenge [237]. Currently, sensors and wearable devices connect real-time data and then transmit the data through a central device, which is called a gateway. The gateway filters the data and transmits only the required data to the central cloud application. Users like patients, doctors, and healthcare providers can access, retrieve, or even monitor the changes through cloud applications to obtain the information they are looking for at any time and from anywhere [238]. However, the rapid proliferation of interconnected IoT devices, networks, and sensors introduces a level of complexity that makes scaling infrastructure without performance degradation particularly difficult. The significant influx of data generated by thousands of IoT devices often overwhelms existing systems, creating bottlenecks that can hinder real-time processing and decision making. Furthermore, resource limitations in cloud and edge computing infrastructures exacerbate these challenges, leading to slower response times, increased latency, and overall inefficiencies in system operations. These inefficiencies not only compromise the quality of patient care but also impede effective data management and timely decision making. [PERSON] et al. designed a framework called BSDMF as an example [239]. According to their experimental results, their framework, including blockchain with distributions, realized a high accuracy ratio and rapid response time. However, their experiment only consists of 100 nodes, from which the conclusion cannot be drawn that the scheme is scalable enough to satisfy expectations. Another challenge related to scalability is using the IoMT with a blockchain. A blockchain is used to protect communication between devices with smart contracts between nodes and proof of work (PoW) to validate the transaction. It is a decentralized distribution technology that enables a peer-to-peer network to increase not only privacy but also scalability [240]. However, applying a blockchain might increase the response time because of the protections applied on top of the data message before transmission. In addition, the \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Category** & **Standards** & **DL Applications (Use Cases)** & **DL Models** \\ \hline \multirow{8}{*}{Wearable Devices} & IEEE 11073 [236], FHIR [225], HL7 [226] & Wearable devices, such as smartwatches and biosensors, detect ECG abnormalities, etc. [25,126]. & CNN, RNN, LSTM \\ \cline{2-4}  & IEEE11073, FHIR, ISO/IEC 27001 [232] & Wearable devices for fall detection [171]. & CNN, YOLO \\ \cline{2-4}  & DICOM [227], FHIR, HL7 & Sleep apnea detection analyzes breathing patterns in sleep monitors [158]. & LSTM, Autoencoders \\ \hline \multirow{8}{*}{Hospital Devices} & ISO 27799 [233], NIST & Detection of cyber threats such as Sybil, replay, blackhole attacks, etc. & GAN, CNN, Autoencoders \\ \cline{2-4}  & GNP, CIN, Autoencoders \\ \cline{2-4}  & ISO/IEC 27001, OAuth 2.0 [234], HIPPA & Biometric authentication for EHR access. & CNN, DeepFace \\ \cline{2-4}  & FHIR, ISO/IEC 27001, GDPR & Federated learning for secure IoMT, keep data locally. & Deep learning embedded in IDS \\ \hline \multirow{8}{*}{Home Devices} & IEEE11073, FHIR, HL7 & Elderly care, smart sensors monitor daily activity, detect abnormal activities, and send alerts [146,147]. & CNN, YOLO, Transformer-based models \\ \cline{1-1} \cline{2-4}  & FHIR, HL7, ISO27799 & Voice assistants, such as detecting speech changes and assisting with medication reminders [54]. & RNN, LSTM, Wav2 Vec \\ \hline \hline \end{tabular}
\end{table} 
Table 13: Deep learning applications in IoMT with standards. access-control list (ACL), which is normally applied in the central server to control the devices, might not be applicable with the introduction of a blockchain, which applies to the P2P instead of server mode [240].