# Explainable AI Assisted IoMT Security in Future 6G Networks
## 8 Conclusions  
The convergence of advanced wireless communication and healthcare technologies presents significant risks to the integrity of sensitive patient data and the performance of medical applications. As the transition to 6G accelerates, the increased integration of virtualization, extended reality, intelligent sensing, and AI into communication frameworks expands the attack surface, increasing risks to patient data integrity and the reliability of life-critical medical applications. Left unaddressed, these emerging threats could result in severe service disruptions and potential catastrophic outcomes in clinical environments. While AI is instrumental in detecting and mitigating complex security threats, its inherent black box nature can hinder transparency, making it difficult for security teams to fully understand and trust automated decisions. This limitation highlights the necessity of XAI in the context of 6G-enabled healthcare systems.  
Our research highlights the crucial role of XAI in closing the explainability gap in AI-driven security systems. By integrating SHAP, LIME, and DiCE, we propose a comprehensive framework that demystifies model decisions and makes security insights both interpretable and actionable for technical and non-technical audiences alike. This multi-perspective analysis enhances the understanding of model behavior, enabling more adaptive and informed security responses. Our approach not only reinforces the consistency and dependability of XAI-based assessments but also underlines the significance of explainability in fostering trust in AI systems, particularly in sensitive and high-stakes environments.  
As healthcare systems advance toward 6G connectivity, the reliability and security of AI-powered medical technologies will be essential for delivering intelligent, timely, and safe patient care. This study supports that goal by contributing to the development of secure, transparent, and adaptive AI models, specifically designed for integration into next-generation healthcare infrastructure. Future work will focus on incorporating datasets specific to 6G, either original or simulated, instead of using 5G-originated datasets, and exploring more advanced models tailored to 6G networks to further enhance the robustness and scalability of AI-based security systems in these evolving environments.  
Conceptualization, L.G. and N.K.; Methodology, L.G. and N.K.; Software, N.K.; Validation, L.G. and N.K.; Formal analysis, N.K.; Investigation, N.K.; Resources, L.G. and N.K.; Data curation, N.K.; Writing--original draft, N.K.; Writing--review & editing, L.G.; Visualization,  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Usage Scenario**} & **XAI Insights (SHAP,** & **Security Risk** & **Mitigation Strategy** & **Security Enhancements** \\  & **LIME, DiCE)** & **Identified** & & **from XAI Insights** \\ \hline \hline \multirow{6}{*}{**Integrated Sensing and Communication (ISAC)**} & & & & SHAP’ identification of high disk and memory usage triggers backup and isolation to prevent \\  & & & & Zero-trust access control, predictive analytics for attack detection, and backup and isolation & ransomware damage. \\  & & & & & LIME’ and DiCE’ insights into heavy traffic and jitter \\  & & & & and isolation & patterns enable predictive anomaly detection, while \\  & & & & & zero-trust access control blocks further \\  & & & & & unauthorized activity. \\ \hline \hline \end{tabular}
\end{table}
Table 9: _Cont._N.K.; Supervision, L.G.; Project administration, L.G. and N.K.; Funding acquisition, L.G. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
The authors declare no conflict of interest.