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

Medical device security requires transparency: The paper demonstrates that explainable AI techniques (SHAP, LIME, and DiCE) can identify vulnerabilities in connected healthcare devices like surgical robots and wearable monitors, making security systems more understandable to healthcare professionals who need to trust and implement them.

Life-or-death stakes demand accountability: Cyberattacks on Internet of Medical Things devices pose direct patient safety risks including surgical errors and equipment failure, underscoring why AI systems protecting these devices must be explainable rather than "black boxes" that clinicians cannot verify or understand.

Next-generation networks need built-in safeguards: As healthcare systems transition to 6G wireless networks with integrated AI and cloud services, the paper argues that explainability should be designed into security systems from the start, rather than added afterward, to prevent new vulnerabilities from emerging.

Transparency builds necessary trust: By making AI security decisions interpretable through explainable AI methods, healthcare organizations can better understand why a system flagged a threat or allowed access, enabling clinicians and administrators to validate that protections are working correctly and not creating false alarms that undermine adoption.