SoK: Security and Privacy Risks of Healthcare AI

TL;DR

This paper systematically reviews security and privacy risks in healthcare AI, highlighting gaps in current research, analyzing attack methods, defenses, and emphasizing the need for cybersecurity advancements in this critical domain.

Contribution

It provides a comprehensive survey and unified framework for healthcare AI security and privacy, identifying under-explored areas and offering experimental insights into threat models.

Findings

Identified key attack vectors and defense strategies in healthcare AI

Highlighted gaps and challenges in current security research

Provided experimental analysis of adversarial attack feasibility

Abstract

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.