# Special Issue on eHealth Innovative Approaches and Applications
## 2 Published Articles
Finally, the generated traffic allowed for the creation of realistic datasets, which are useful for the development of ML, and anomaly detection techniques. [PERSON] et al. (contribution 6) investigated the use of Large Language Models (LLMs) for coding inpatient diagnoses. ICD coding is very useful for several tasks, ranging from statistical analysis to clinical research or medical billing, as well as for ensuring and improving the interoperability of medical documents. Moreover, the application of ICD coding is often mandatory for several official documents in many countries. The automatic application of ICD coding is not an easy task due to the very large number of codes; moreover, the complexity of clinical and medical language, which is used in many documents that must be tagged with the corresponding ICD codes, makes this task more difficult. The paper presented an analysis of the performance of three LLMs (mBERT [30], Multilingual E5 [31] and MEDPSU-RoBERTa [32]), examining their performances on a very large Thai language dataset formed from inpatient admissions. The results showed satisfactory results and confirmed that MEDPSU-RoBERTa, which is a domain-specific model, is able to achieve the best results. The results also highlighted that the performances are strictly dependent on the datasets, obtaining more accurate results in the cases of codes with larger number of samples, while the rarity of certain diseases (and corresponding documents in the datasets) posed challenges to accurate coding. Finally, the results underlined the difficulties when the models are faced with long clinical documents. [PERSON] et al. (contribution 7) produced work focused on the security of IoMT devices, presenting a systematic literature review of Risk Assessment approaches specifically devoted to the IoMT. IoMT devices can be very effective instruments in supporting the remote management and care of patients; on the other hand, as they are able to collect and transmit sensitive data, they have critical and strict security requirements. While classic cyber security Risk Assessment approaches can be exploited to identify and assess potential risks, they are not able to manage sophisticated cyber-attacks in near real time. In these latter cases, Dynamic Risk Assessment (DRA) approaches [33] are more suitable for tackling the risks to patients when using IoMT devices. The authors produced a detailed literature review of RA related to the IoMT, highlighting the current trends and the more recent approaches in this field. In more detail, the review first focused on the significant ways of mitigating the impact of unauthorised intrusions, protecting end-users from personal data leakage and ensuring the device usage continuity. Furthermore, the paper identified the main research directions for DRA, addressing the challenges related to the dynamic infrastructures and the uncertain attack surfaces, with the aim of improving user protection and preventing cyber attacks. The contribution from [PERSON] et al. (contribution 8) described a DICOM-based medical image communication system named Medical Imaging and Diagnostics on the Move (MIDOM). The proposed system includes some enhancements based on evolutions of the custom lossless Classification and Blending Predictor Coder (CBPC) [34] compression method previously developed by the same authors. Moreover, the MIDOM system was also integrated with Orthanc [35], a lightweight DICOM server, and a medical images-storing PACS server. The proposed system was tested on five real-world anonymised medical image sets, evaluating compression ratios and latency reduction, with the aim of simulating scenarios where medical service availability might be severely limited. The obtained results in all scenarios demonstrated that MIDOM, exploiting the included compression methods, was able to lower the network latency by at least 60% with respect to the transmission of raw and uncompressed image sets, allowing, at the same time, the perfect reconstruction of medical images. [PERSON] et al.