A Survey of Threats to Research Literature-dependent Medical AI Solutions

• Published in: ACM computing surveys Vol. 55; no. 14s; pp. 1 - 26
• Main Authors: Saini, Shalini, Saxena, Nitesh
• Format: Journal Article
• Language: English
• Published: New York, NY ACM 31.12.2023
• Subjects: Applied computing; Computing methodologies; General and reference; Health care information systems; Information accountability and usage control; Information extraction; Knowledge representation and reasoning; Logical and relational learning; Medical technologies; Security and privacy; Social and professional topics; Surveys and overviews; trustworthy medical technologies; NLP; Predatory science; data pollution; semantic analysis; data integrity; research literature; Medical Artificial Intelligence; Knowledge Graph
• ISSN: 0360-0300; 1557-7341
• DOI: 10.1145/3592597

Medical Artificial Intelligence (MedAI) harnesses the power of medical research through AI algorithms and vast data to address healthcare challenges. The security, integrity, and credibility of MedAI tools are paramount, because human lives are at stake. Predatory research, in a culture of “publish or perish,” is exploiting the “pay for publish” model to infiltrate he research literature repositories. Although, it is challenging to measure the actual predatory research induced data pollution and patient harm, our work shows that the breached integrity of MedAI inputs is a serious threat to trust the MedAI output. We review a wide range of research literature discussing the threats of data pollution in the research literature, feasible attacks impacting MedAI solutions, research literature-based tools, and influence on healthcare. Our contribution lies in presenting a comprehensive literature review, addressing the gap of predatory research vulnerabilities affecting MedAI solutions, and helping to develop robust MedAI solutions in the future.