Explainable AI for Securing Healthcare in IoT-Integrated 6G Wireless Networks

As healthcare systems increasingly rely on advanced wireless networks and connected devices, ensuring the security of medical applications has become a critical concern. The integration of Internet of Medical Things (IoMT) devices with real - time health monitoring and care delivery has revolutionized patient care but has also introduced new security vulnerabilities. Each connected device, whether it is a part of a robotic surgical arm, intensive care equipment, or a wearable health monitor, serves as a poten tial entry point for cyberattacks. Such vulnerabilities could lead to life threatening consequences like poorly performed surgeries, malfunctioning of life support systems or incorrect treatment due to data breache s . The ITU IMT - 2030 framework envisions that 6G will be transforming healthcare through massive connectivity, AI, and cloud integration. However, it may also introduce new security vulnerabilities that can threaten the patient safety and privacy. Therefore, a ddressing these threats requires a thor ough reassessment of security measures . This paper presents an innovative use of explainable AI (XAI) techniques - such as SHAP, LIME, and DiCE - to identify vulnerabilities, strengthen security measures, and enhance both security and transparency within the 6G healthcare ecosystem, ensuring robust protection and trust . In addition to the theoretical background, this paper presents experimental analysis and the authors very positive findings.