掠夺性医学：探索和测量医学AI对掠夺性科学的脆弱性
Predatory Medicine: Exploring and Measuring the Vulnerability of Medical AI to Predatory Science
摘要：
医疗人工智能（MedAI）用于诊断、治疗选择和药物开发，代表了医疗保健的新时代。MedAI工具的安全性、完整性和可信度是最重要的问题，因为这关系到人类的生命。MedAI解决方案通常严重依赖科学医学研究文献作为主要数据源，吸引攻击者的注意力作为潜在目标。我们提出了第一个研究如何MedAI的输出可以被污染的掠夺性出版物存在（PPP）。我们研究了两个MedAI系统：mediKanren（疾病独立）和CancerMine（疾病特异性），使用研究文献作为来自研究存储库PubMed、PubMed衍生数据库SemMedDB和NIH翻译知识图谱（KG）的主要数据输入。我们的研究有三个方面的重点：（1）在PubMed中识别PPP;（2）在SemMedDB和KG中验证PPP;（3）证明PPP遍历到MedAI输出的现有漏洞。我们的贡献在于识别MedAI输入中现有的PPP，并展示掠夺性科学如何危及MedAI解决方案的可信度，使其在现实生活中的部署受到质疑。
英文摘要：
Medical Artificial Intelligence (MedAI) for diagnosis, treatment options, and drug development represents the new age of healthcare. The security, integrity, and credibility of MedAI tools are paramount issues because human lives are at stake. MedAI solutions are often heavily dependent on scientific medical research literature as a primary data source that draws the attacker's attention as a potential target. We present a first study of how the output of MedAI can be polluted with Predatory Publications Presence (PPP). We study two MedAI systems: mediKanren (disease independent) and CancerMine (Disease-specific), which use research literature as primary data input from the research repository PubMed, PubMed derived database SemMedDB, and NIH translational Knowledge Graphs (KGs). Our study has a three-pronged focus: (1) identifying the PPP in PubMed; (2) verifying the PPP in SemMedDB and the KGs; (3) demonstrating the existing vulnerability of PPP traversing to the MedAI output. Our contribution lies in identifying the existing PPP in the MedAI inputs and demonstrating how predatory science can jeopardize the credibility of MedAI solutions, making their real-life deployment questionable.
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