Szczegóły publikacji
Opis bibliograficzny
A study on vulnerability analysis process of generative AI-based digital medical contents / Hoon Ko, Libor Mesicek, Marek R. OGIELA, Yongyun Cho // Internet of Things ; ISSN 2543-1536. — 2025 — vol. 34 art. no. 101759, s. 1-15. — Bibliogr. s. 15, Abstr. — Publikacja dostępna online od: 2025-09-12
Autorzy (4)
- Ko Hoon
- Mesicek Libor
- AGHOgiela Marek
- Cho Yongyun
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 162795 |
|---|---|
| Data dodania do BaDAP | 2025-09-29 |
| Tekst źródłowy | URL |
| DOI | 10.1016/j.iot.2025.101759 |
| Rok publikacji | 2025 |
| Typ publikacji | artykuł w czasopiśmie |
| Otwarty dostęp |  |
| Creative Commons | |
| Czasopismo/seria | Internet of Things |
Abstract
This paper conducts a sequential analysis of the security vulnerabilities associated with AI-generated digital medical content across ten key areas and presents strategies to enhance the safety and reliability of medical AI systems. The study comprehensively examines aspects such as the quality and integrity of digital content, risks of privacy exposure, model security vulnerabilities, system security, ethical risks, performance stability, regulatory compliance, interoperability, and disaster recovery capabilities. To evaluate the AI system’s vulnerabilities, quantitative metrics such as Data Accuracy (DA), Personal Information Risk (PIR), and Model Robustness (MR) are utilized. The results underscore the importance of strengthening encryption, improving backup systems, and enhancing defenses against adversarial attacks. These findings highlight the critical need for reinforcing security protocols, adhering to ethical standards, and ensuring strict compliance with international regulations. The study offers vital guidelines for developing secure AI systems that can be effectively integrated into medical applications, contributing to the safe and reliable use of generative AI technology in healthcare settings.
