# Explainable AI Assisted IoMT Security in Future 6G Networks
## 2 Review of Existing Research and Novel Contributions  
### Existing Research  
Recent studies have increasingly focused on the integration of XAI with healthcare, with the aim of improving data privacy, interpretability, and clinical decision-making. For instance, the authors in [6] propose a framework that combines XAI with mass surveillance to improve epidemic monitoring. This framework uses deep learning and edge computing to improve data privacy through blockchain integration. In [7], the authors apply XAI to heart disease diagnosis and emphasize the need for transparent AI models to foster trust among medical professionals. Techniques such as LIME and SHAP are used to illustrate how specific clinical features influence predictions. Similarly, paper [8] presents a framework that combines federated learning (FL) with XAI to boost both privacy and interpretability in next-generation healthcare networks, resulting in enhanced prediction accuracy and user trust.  
Researchers have employed XAI to explain clinical diagnoses. In paper [9], authors use SHAP to support clinicians in understanding the predictive factors in unplanned hospital readmissions of elderly patients. In [20], the authors use XAI to address safety, trust, and reliability concerns regarding using AI in brain tumor diagnosis. Similarly, Ref. [21] explores the integration of XAI with neuromorphic computing to develop consumer-friendly healthcare applications, using techniques such as SHAP, LIME, and ELI5 to overcome transparency limitations. Several studies focus on XAI in security-sensitive healthcare settings. For instance, in [22], authors propose an explainable malicious traffic detection system using LIME, SHAP, ELI5, and Integrated Gradients (IGs) to ensure interpretability in models monitoring intensive care patient datasets. Similarly, Refs. [23; 24] present intrusion detection systems in IoMT environments, both using the WUSTL-EHMS-2020 dataset and SHAP to explain deep learning model decisions. Finally, Ref. [25] introduces a privacy-preserving ECG classification framework based on federated learning, incorporating XAI to assist healthcare professionals in validating and interpreting model predictions.  
While many studies emphasize the role of XAI in healthcare, they often overlook the deeper integration of wireless networks and the emerging security challenges associated with the impending transition from 5G to 6G. Furthermore, due to the developmental stage of 6G, contemporary research to fully investigate how XAI can be incorporated into 6G-enabled healthcare environments--where dynamic system parameters and AI-driven security features are critical--is largely missing. Additionally, to the best of our knowledge, no prior work has leveraged multiple XAI techniques in combination for cross-validation, which is a critical step in ensuring the reliability and consistency of AI-generated explanations. Moreover, the current research lacks a strategic framework for translating insights gained from XAI into actionable risk mitigation strategies that are specifically tailored to 6G usage scenarios in the healthcare domain.  
Our research addresses these critical gaps by carrying out the following steps: comprehensively mapping 6G healthcare use cases using the ITU-R IMT-2030 framework; applying and cross-validating multiple XAI methods (SHAP, LIME, and DiCE) to uncover consistent and trustworthy feature attributions; linking these insights to security threats and performance bottlenecks within each 6G usage scenario; and proposing XAI-driven mitigation strategies and enhancements that empower administrators to take targeted, scenario-specific security action in 6G medical IoT environments.