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

IF 7.6 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Internet of Things Pub Date : 2025-09-12 DOI:10.1016/j.iot.2025.101759

Hoon Ko , Libor Mesicek , Marek R. Ogiela , Yongyun Cho

{"title":"A study on vulnerability analysis process of generative AI-based digital medical contents","authors":"Hoon Ko , Libor Mesicek , Marek R. Ogiela , Yongyun Cho","doi":"10.1016/j.iot.2025.101759","DOIUrl":null,"url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"34 ","pages":"Article 101759"},"PeriodicalIF":7.6000,"publicationDate":"2025-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Internet of Things","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2542660525002720","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}

引用次数: 0

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.

查看原文本刊更多论文

基于生成式人工智能的数字医疗内容脆弱性分析过程研究

本文对人工智能生成的数字医疗内容在十个关键领域的安全漏洞进行了顺序分析，并提出了提高医疗人工智能系统安全性和可靠性的策略。该研究全面考察了数字内容的质量和完整性、隐私暴露风险、模型安全漏洞、系统安全性、道德风险、性能稳定性、法规遵从性、互操作性和灾难恢复能力等方面。为了评估人工智能系统的漏洞，使用了数据准确性（DA）、个人信息风险（PIR）和模型鲁棒性（MR）等定量指标。研究结果强调了加强加密、改进备份系统和增强防御对抗性攻击的重要性。这些发现突出了加强安全协议、遵守道德标准和确保严格遵守国际法规的迫切需要。该研究为开发安全的人工智能系统提供了重要指南，这些系统可以有效地集成到医疗应用中，有助于在医疗保健环境中安全可靠地使用生成式人工智能技术。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Internet of Things Multiple-

CiteScore

3.60

自引率

5.10%

发文量

115

审稿时长

37 days

期刊介绍： Internet of Things; Engineering Cyber Physical Human Systems is a comprehensive journal encouraging cross collaboration between researchers, engineers and practitioners in the field of IoT & Cyber Physical Human Systems. The journal offers a unique platform to exchange scientific information on the entire breadth of technology, science, and societal applications of the IoT. The journal will place a high priority on timely publication, and provide a home for high quality. Furthermore, IOT is interested in publishing topical Special Issues on any aspect of IOT.