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

arXiv: https://arxiv.org/abs/2505.14659 · HTML: https://mlsanity.com/paper/2505.14659/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 21 references that are in the map.
- First posted on arXiv 20 May 2025.

## Builds on (most cited first, 7 of 7)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Explaining Machine Learning Classifiers through Diverse Counterfactual Explanations](https://mlsanity.com/paper/1905.07697/) (2019)
- [Designing ECG Monitoring Healthcare System with Federated Transfer Learning and Explainable AI](https://mlsanity.com/paper/2105.12497/) (2021)
- [Explainable AI meets Healthcare: A Study on Heart Disease Dataset](https://mlsanity.com/paper/2011.03195/) (2020)
- [A Survey on Security and Privacy Issues in Modern Healthcare Systems: Attacks and Defenses](https://mlsanity.com/paper/2005.07359/) (2020)
- [An Approach To Enhance IoT Security In 6G Networks Through Explainable AI](https://mlsanity.com/paper/2410.05310/) (2024)

## Abstract (arXiv)

As healthcare systems increasingly adopt advanced wireless networks and connected devices, securing medical applications has become critical. The integration of Internet of Medical Things devices, such as robotic surgical tools, intensive care systems, and wearable monitors has enhanced patient care but introduced serious security risks. Cyberattacks on these devices can lead to life threatening consequences, including surgical errors, equipment failure, and data breaches. While the ITU IMT 2030 vision highlights 6G's transformative role in healthcare through AI and cloud integration, it also raises new security concerns. This paper explores how explainable AI techniques like SHAP, LIME, and DiCE can uncover vulnerabilities, strengthen defenses, and improve trust and transparency in 6G enabled healthcare. We support our approach with experimental analysis and highlight promising results.