Predatory Medicine: Exploring and Measuring the Vulnerability of Medical AI to Predatory Science

Abstract

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.Data and Code Availability This paper uses the data and code from PubMed , NIH SemMedDB , NIH Translational KGs (provided by mediKanren team), CancerMine Zenodo repository, and mediKanren Github