A study on vulnerability analysis process of generative AI-based digital medical contents

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F44555601%3A13510%2F25%3A43899387" target="_blank" >RIV/44555601:13510/25:43899387 - isvavai.cz</a>
Result on the web
<a href="https://www.sciencedirect.com/science/article/pii/S2542660525002720?via%3Dihub" target="_blank" >https://www.sciencedirect.com/science/article/pii/S2542660525002720?via%3Dihub</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.iot.2025.101759" target="_blank" >10.1016/j.iot.2025.101759</a>

Result language
angličtina
Original language name
A study on vulnerability analysis process of generative AI-based digital medical contents
Original language description
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.
Czech name
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Czech description
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Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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