## 4 Securing Artificial Intelligence Accompanying the WBAN  
In future clinical environments, it is expected that an increasing number of multimodal patient data will be automatedly consolidated and analyzed. Such holistic systems, which are expected to support the work of clinical personnel, are particularly enabled by advances in the field of artificial intelligence. Apart from the support in terms of decision-making and diagnosis within the WBAN, which provides multiple attack surfaces for outside attackers, AI can also be used as a defense mechanism. The latter is discussed briefly, concerning the limited computation power of WBAN sensors. Intrusion detection for networks is a particularly complex task that is often tackled via deep learning techniques [33, 34], whereas there are some efforts regarding IoT environments, the limited computational power of the sensors and sensor carriers prevent the efficient implementation of large deep learning models with multiple hidden layers or sophisticated architectures. Recurrent neural networks are an especially successful technology for intrusion detection but require massive computational overhead not feasible for IoT systems [33]. More suitable for intrusion detection regarding the computational complexity within the WBAN are machine learning methods as proposed in several recent works, e.g., Support Vector Machines [35, 36, 37]. [PERSON] et al. present an intruder detection system specifically designed for WBANs based on Decision Trees which, if the employed sensors produce data with only little noise, can outperform Neural Networks [38]. In the following, the AI applications within the WBAN are discussed. These can be used for diagnosis and therapy recommendations, but offer attack vectors for intruders. Thus, there is a conflict of interest between the potential diagnosis and therapy recommendation improvements, which directly benefit the individual patient and data security and privacy concerns. Those are both addressed, for instance, by the EU in the General Data Protection Regulation (GDPR) [39] and the European AI Act [5]. The GDPR does not specifically discuss WBANs but still contains implications. With the scope of holistic, global AI frameworks, there could be issues that are not yet fully addressed by regulatory frameworks, One principle is the purpose limitation, which might limit the use of collected patient data to benefit an individual patient. This contradicts the idea of AI models that benefit from many different datasets from several locations. Whether the potential diagnosis and treatment benefits outweigh privacy concerns will be an ongoing discussion as AI increases in performance in the future. \begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Communication** & **Physical Unclonable** & **Implicit Certificates** & **Symmetric Crypto-Based** & **Biometrics** \\
**Channel** & **Functions** & & **Auth.** & \\ \hline HBC Channel & Ideal for device auth.; high security & Limited by proximity & Suitable for key exchange & Effective for user auth. \\ Wireless Cyclic & Enhances device & Suitable for trust chains & Optimal for data in transit & Feasible for user auth. \\ Channel & fingerprinting; adds complexity & & & \\ Edge Device & Useful for device & Critical for secure network & Standard for data & User verification \\ Channel & fingerprinting & interactions & protection & before data exit \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Authentication mechanisms across WBAN communication channels. Depending on which layer within the WBAN clinic infrastructure artificial intelligence is deployed, different security requirements arise. An overview of the application layers of AI and most anticipated attack vectors are shown in Figure 3. In the following, the role of AI on each layer, as well as the most prominent attack vectors, are discussed. Especially in the advent of the AI Act of the European Union, it is necessary to understand, assess, and mitigate underlying risks, as the data in the healthcare sector are always considered confidential.