The use of AI in detecting fraud within the healthcare industry represents a significant advancement in combating the escalating issue of fraud in this domain. Contemporary artificial intelligence technologies, including machine learning, natural language processing, and deep learning, assist in economically safeguarding healthcare resources against fraud and ensuring that patients receive their rightful entitlements. Current research on security and privacy (S&P) in healthcare AI is markedly imbalanced regarding deployment scenarios and threat models, and has a disjointed focus from the biomedical research community. This inhibits a thorough understanding of healthcare AI threats. This paper examines healthcare AI research and provides a framework to identify under-explored areas, addressing the gap. We provide a comprehensive analysis of healthcare AI attacks and countermeasures, highlighting problems and research potential for each AI-driven healthcare application domain. Our experimental examination of threat models and feasibility studies on under-explored adversarial assaults highlights the urgent need for cybersecurity research in the fast-developing healthcare AI area.
Raihan et al. (Wed,) studied this question.
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