  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.
