Explainable AI for Securing Healthcare in IoT-Integrated 6G Wireless Networks
Fuente:
arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866912384385286144 |
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| author | Kaur, Navneet Gupta, Lav |
| author_facet | Kaur, Navneet Gupta, Lav |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_14659 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Explainable AI for Securing Healthcare in IoT-Integrated 6G Wireless Networks Kaur, Navneet Gupta, Lav Machine Learning Artificial Intelligence 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. |
| title | Explainable AI for Securing Healthcare in IoT-Integrated 6G Wireless Networks |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2505.14659 |