论文标题
掠夺性医学：探索和衡量医学AI对掠食性科学的脆弱性
Predatory Medicine: Exploring and Measuring the Vulnerability of Medical AI to Predatory Science
论文作者
论文摘要
用于诊断，治疗选择和药物开发的医疗人工智能（MEDAI）代表了医疗新时代。 Medai工具的安全性，完整性和信誉是最重要的问题，因为人类的生命受到威胁。 Medai解决方案通常在很大程度上取决于科学医学研究文献作为主要数据源，它吸引了攻击者的注意力作为潜在的目标。我们首先研究了如何通过掠夺性出版物（PPP）污染Medai的产出。我们研究了两个Medai系统：Medikanren（疾病独立）和取消（疾病特异性），它们使用研究文献作为研究存储库PubMed，PubMed衍生数据库SEMMedDB和NIH转化知识图（KGS）的主要数据输入。我们的研究具有三方面的重点：（1）在PubMed中识别PPP； （2）在SemmedDB和KGS中验证PPP； （3）证明PPP遍历Medai输出的现有漏洞。我们的贡献在于确定MEDAI投入中现有的PPP，并证明掠夺性科学如何危及Medai解决方案的信誉，从而使他们的现实生活部署值得怀疑。
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.
