SoK: Security and Privacy Risks of Medical AI
<|reference_start|>SoK: Security and Privacy Risks of Medical AI: The integration of technology and healthcare has ushered in a new era where software systems, powered by artificial intelligence and machine learning, have become essential components of medical products and services. While these advancements hold great promise for enhancing patient care and healthcare delivery efficiency, they also expose sensitive medical data and system integrity to potential cyberattacks. This paper explores the security and privacy threats posed by AI/ML applications in healthcare. Through a thorough examination of existing research across a range of medical domains, we have identified significant gaps in understanding the adversarial attacks targeting medical AI systems. By outlining specific adversarial threat models for medical settings and identifying vulnerable application domains, we lay the groundwork for future research that investigates the security and resilience of AI-driven medical systems. Through our analysis of different threat models and feasibility studies on adversarial attacks in different medical domains, we provide compelling insights into the pressing need for cybersecurity research in the rapidly evolving field of AI healthcare technology.<|reference_end|>
@article{chang2024sok:,
    title={SoK: Security and Privacy Risks of Medical AI},
    author={Yuanhaur Chang, Han Liu, Evin Jaff, Chenyang Lu, Ning Zhang},
    journal={arXiv preprint arXiv:2409.07415},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.07415},
    primaryClass={cs.CR cs.AI cs.LG}
}