SoK: Security and Privacy Risks of Medical AI

• URL: http://arxiv.org/abs/2409.07415v1
• Date: Wed, 11 Sep 2024 16:59:58 GMT
• Title: SoK: Security and Privacy Risks of Medical AI
• Authors: Yuanhaur Chang, Han Liu, Evin Jaff, Chenyang Lu, Ning Zhang,
• Abstract summary: 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. This paper explores the security and privacy threats posed by AI/ML applications in healthcare.
• Score: 14.592921477833848
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• Cybersecurity and Frequent Cyber Attacks on IoT Devices in Healthcare: Issues and Solutions [0.0]
  Internet of Things (IoT) devices in healthcare have revolutionized patient care, offering improved monitoring, diagnostics, and treatment. However, the proliferation of these devices has also introduced significant cybersecurity challenges. This paper reviews the current landscape of cybersecurity threats targeting IoT devices in healthcare, discusses the underlying issues contributing to these vulnerabilities, and explores potential solutions.
  arXiv  Detail & Related papers  (2025-01-20T03:29:07Z)
• From Screens to Scenes: A Survey of Embodied AI in Healthcare [31.183244202702983]
  "EmAI in healthcare" spans diverse fields such as algorithms, robotics, and biomedicine. We provide a comprehensive overview of the "brain" of EmAI for healthcare, wherein we introduce AI algorithms for perception, actuation, planning, and memory. We discuss the technical barriers and explore ethical considerations, offering a forward-looking perspective on the future of EmAI in healthcare.
  arXiv  Detail & Related papers  (2025-01-13T16:35:52Z)
• Open Problems in Machine Unlearning for AI Safety [61.43515658834902]
  Machine unlearning -- the ability to selectively forget or suppress specific types of knowledge -- has shown promise for privacy and data removal tasks. In this paper, we identify key limitations that prevent unlearning from serving as a comprehensive solution for AI safety.
  arXiv  Detail & Related papers  (2025-01-09T03:59:10Z)
• Towards Next-Generation Medical Agent: How o1 is Reshaping Decision-Making in Medical Scenarios [46.729092855387165]
  We study the choice of the backbone LLM for medical AI agents, which is the foundation for the agent's overall reasoning and action generation. Our findings demonstrate o1's ability to enhance diagnostic accuracy and consistency, paving the way for smarter, more responsive AI tools.
  arXiv  Detail & Related papers  (2024-11-16T18:19:53Z)
• Explainable Artificial Intelligence for Medical Applications: A Review [42.33274794442013]
  This article reviews recent research grounded in explainable artificial intelligence (XAI) It focuses on medical practices within the visual, audio, and multimodal perspectives. We endeavour to categorise and synthesise these practices, aiming to provide support and guidance for future researchers and healthcare professionals.
  arXiv  Detail & Related papers  (2024-11-15T11:31:06Z)
• Safety challenges of AI in medicine in the era of large language models [23.817939398729955]
  Large language models (LLMs) offer new opportunities for medical practitioners, patients, and researchers. As AI and LLMs become more powerful and especially achieve superhuman performance in some medical tasks, public concerns over their safety have intensified. This review examines emerging risks in AI utilization during the LLM era.
  arXiv  Detail & Related papers  (2024-09-11T13:47:47Z)
• Testing autonomous vehicles and AI: perspectives and challenges from cybersecurity, transparency, robustness and fairness [53.91018508439669]
  The study explores the complexities of integrating Artificial Intelligence into Autonomous Vehicles (AVs) It examines the challenges introduced by AI components and the impact on testing procedures. The paper identifies significant challenges and suggests future directions for research and development of AI in AV technology.
  arXiv  Detail & Related papers  (2024-02-21T08:29:42Z)
• Progression and Challenges of IoT in Healthcare: A Short Review [0.0]
  The burgeoning field of smart healthcare is poised to generate substantial revenue in the foreseeable future. Numerous nations have strategically deployed the Internet of Medical Things (IoMT) alongside other measures to combat the propagation of COVID-19. The rapid and extensive adoption of IoMT worldwide has magnified issues related to security and privacy.
  arXiv  Detail & Related papers  (2023-11-11T08:38:04Z)
• White paper on cybersecurity in the healthcare sector. The HEIR solution [1.3717071154980571]
  Patient data, including medical records and financial information, are at risk, potentially leading to identity theft and patient safety concerns. The HEIR project offers a comprehensive cybersecurity approach, promoting security features from various regulatory frameworks. These measures aim to enhance digital health security and protect sensitive patient data while facilitating secure data access and privacy-aware techniques.
  arXiv  Detail & Related papers  (2023-10-16T07:27:57Z)
• FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare [73.78776682247187]
  Concerns have been raised about the technical, clinical, ethical and legal risks associated with medical AI. This work describes the FUTURE-AI guideline as the first international consensus framework for guiding the development and deployment of trustworthy AI tools in healthcare.
  arXiv  Detail & Related papers  (2023-08-11T10:49:05Z)
• Adversarial Machine Learning Attacks and Defense Methods in the Cyber Security Domain [58.30296637276011]
  This paper summarizes the latest research on adversarial attacks against security solutions based on machine learning techniques. It is the first to discuss the unique challenges of implementing end-to-end adversarial attacks in the cyber security domain.
  arXiv  Detail & Related papers  (2020-07-05T18:22:40Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.