## 5 Challenges
### Interoperability
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Type of Attack** & **Description** & **Solution Method** \\ \hline Data poisoning & \begin{tabular}{c} Injecting malicious data into the dataset used for \\ training to degrade model performance. \\ \end{tabular} & \begin{tabular}{c} Implement robust data validation and use \\ differential privacy during training. \\ \end{tabular} \\ \hline Eavesdropping & \begin{tabular}{c} Interception of data during transmission \\ between IoMT devices and servers. \\ \end{tabular} & \begin{tabular}{c} Use encryption for secure end-to-end data \\ \begin{tabular}{c} Adversarial attacks \\ \end{tabular} & \begin{tabular}{c} Manipulating inputs to cause DL models to \\ make incorrect predictions. \\ \end{tabular} & \begin{tabular}{c} Deploy adversarial training and use input \\ sanitization techniques. \\ \end{tabular} \\ \hline Denial of service (DoS) & Flooding the network with traffic to disrupt & \begin{tabular}{c} Implement network traffic monitoring and \\ distributed denial-of-service (DDoS) mitigation \\ strategies. \\ \end{tabular} \\ \hline Man in the middle (MitM) & \begin{tabular}{c} Intercepting and altering communication \\ between IoMT devices. \\ \end{tabular} & \begin{tabular}{c} Use mutual authentication and encryption for \\ secure communication. \\ \end{tabular} \\ \hline Model inversion & \begin{tabular}{c} Reconstructing sensitive training data from the \\ output of learning model. \\ \end{tabular} & \begin{tabular}{c} Apply differential privacy and limit access to \\ model outputs. \\ \end{tabular} \\ \hline Membership inference & \begin{tabular}{c} Determining if specific data points were used \\ during training. \\ \end{tabular} & \
\begin{tabular}{c} Use privacy-preserving techniques like federated \\ learning and noise injection. \\ \end{tabular} \\ \hline Ransomware & Encrypting data or systems and demanding & Maintain regular backups and implement strong \\ payment for decryption. &
\begin{tabular}{c} access controls. \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 11: IoMT network attacks and solution methods. prise (IHE) [228]: A framework that integrates existing standards like HL7 and DICOM for seamless healthcare IT communication. (v) ANSI ASC X12 [229]: A standard for electronic data interchange (EDI), commonly used in insurance claims and billing. Security and privacy standards related to IoMT include the following. (i) Health Insurance Portability and Accountability Act (HIPPA) [230]: A regulation in the USA to ensure patient data privacy and security. (ii) General Data Protection Regulation (GDPR) [231] European regulation for protecting patient data in healthcare applications. (iii) ISO27001 [232] A standard for managing information security, applicable to healthcare IT. (iv) ISO 27799 [233]: A global set of guidelines for information security in healthcare organizations. (v) OAuth 2.0 [234] & OpenID Connect [235]: Aiming to enable secured authentication and authorization in healthcare applications. The most important medical device and IoMT standard is IEEE 11073 [236], a standard for network communication between medical devices and healthcare IT systems. According to device deployment with DL, we match the above standards with DL applications to demonstrate how DL can be utilized in the interoperability area. Using wearable devices as an example, DL models such as CNN, RNN, and LSTM are used to detect abnormal activities or predict the potential risks, as Table 13 indicates. Beyond technical challenges, there are significant ethical and legal concerns. Bias in DL models, often stemming from non-diverse or unrepresentative training datasets, raises questions about fairness and equity in healthcare delivery, particularly when AI is involved in critical decision making. This bias can lead to misdiagnoses or unequal treatment outcomes for specific populations, undermining trust in AI-driven systems. Moreover, ethical dilemmas arise when AI overrides human judgment or operates without clear accountability frameworks.