梳理医疗AI安全隐私风险，揭示研究盲区与攻防挑战
SoK: Security and Privacy Risks of Healthcare AI
- 构建统一框架，系统分析医疗AI攻防场景
- 发现现有研究在威胁模型与应用落地间脱节
- 针对未充分探索的攻击类型提出可行性验证
人工智能与机器学习在医疗系统中的应用虽有望提升诊疗效率，但也使敏感数据与系统完整性面临网络攻击风险。当前医疗AI安全与隐私研究在部署场景和威胁模型上严重失衡，且与生物医学研究界缺乏联动，阻碍了对医疗AI潜在风险的全面理解。本文通过全面审视现有研究，提出一个统一框架，以识别未被充分探索的领域。系统梳理了医疗AI的各类攻击与防御手段，并指出现有研究在各应用场景中的挑战与机遇。通过对多种威胁模型的实验分析及对未充分研究的对抗性攻击的可行性研究，揭示了医疗AI领域亟需加强网络安全研究的紧迫性。
原文摘要 · Abstract (English)
The integration of artificial intelligence (AI) and machine learning (ML) into healthcare systems holds great promise for enhancing patient care and care delivery efficiency; however, it also exposes sensitive data and system integrity to potential cyberattacks. Current security and privacy (S&P) research on healthcare AI is highly unbalanced in terms of healthcare deployment scenarios and threat models, and has a disconnected focus with the biomedical research community. This hinders a comprehensive understanding of the risks that healthcare AI entails. To address this gap, this paper takes a thorough examination of existing healthcare AI S&P research, providing a unified framework that allows the identification of under-explored areas. Our survey presents a systematic overview of healthcare AI attacks and defenses, and points out challenges and research opportunities for each AI-driven healthcare application domain. Through our experimental analysis of different threat models and feasibility studies on under-explored adversarial attacks, we provide compelling insights into the pressing need for cybersecurity research in the rapidly evolving field of healthcare AI.
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