SoK: Security and Privacy Risks of Healthcare AI

• URL: http://arxiv.org/abs/2409.07415v2
• Date: Wed, 30 Apr 2025 22:27:30 GMT
• Title: SoK: Security and Privacy Risks of Healthcare AI
• Authors: Yuanhaur Chang, Han Liu, Chenyang Lu, Ning Zhang,
• Abstract summary: The integration of artificial intelligence (AI) and machine learning (ML) into healthcare systems holds great promise for enhancing patient care.<n>However, it also exposes sensitive data and system integrity to potential cyberattacks.<n>Current security and privacy (S&P) research on healthcare AI is highly unbalanced in terms of healthcare deployment scenarios and threat models.
• Score: 15.655956766190256
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Towards Privacy-aware Mental Health AI Models: Advances, Challenges, and Opportunities [61.633126163190724]
  Mental illness is a widespread and debilitating condition with substantial societal and personal costs. Recent advances in Artificial Intelligence (AI) hold great potential for recognizing and addressing conditions such as depression, anxiety disorder, bipolar disorder, schizophrenia, and post-traumatic stress disorder. Privacy concerns, including the risk of sensitive data leakage from datasets and trained models, remain a critical barrier to deploying these AI systems in real-world clinical settings.
  arXiv  Detail & Related papers  (2025-02-01T15:10:02Z)
• 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)
• Trust and Dependability in Blockchain & AI Based MedIoT Applications: Research Challenges and Future Directions [0.0]
  This paper critically reviews the integration of Artificial Intelligence (AI) and blockchain technologies in the context of Medical Internet of Things (MedIoT) applications. By examining current research, we underscore AI's potential in advancing diagnostics and patient care, alongside blockchain's capacity to bolster data security and patient privacy.
  arXiv  Detail & Related papers  (2025-01-05T20:21:22Z)
• Implications of Artificial Intelligence on Health Data Privacy and Confidentiality [0.0]
  The rapid integration of artificial intelligence in healthcare is revolutionizing medical diagnostics, personalized medicine, and operational efficiency. However, significant challenges arise concerning patient data privacy, ethical considerations, and regulatory compliance. This paper examines the dual impact of AI on healthcare, highlighting its transformative potential and the critical need for safeguarding sensitive health information.
  arXiv  Detail & Related papers  (2025-01-03T05:17:23Z)
• Towards AI-enabled Cyber Threat Assessment in the Health Sector [0.0]
  The aim of this project is to introduce an AI-enabled platform that collects security relevant information from the outside of a health organization. The platform delivers a risk score and supports decision makers in healthcare institutions to optimize investment choices for security measures.
  arXiv  Detail & Related papers  (2024-09-19T13:34:34Z)
• Safety challenges of AI in medicine [23.817939398729955]
  Review examines potential risks in AI practices that may compromise safety in medicine. Examines reduced performance across diverse populations, inconsistent operational stability, the need for high-quality data for effective model tuning, and the risk of data breaches during model development and deployment. Second part of this article explores safety issues specific to large language models (LLMs) in medical contexts.
  arXiv  Detail & Related papers  (2024-09-11T13:47:47Z)
• AI-Driven Healthcare: A Survey on Ensuring Fairness and Mitigating Bias [2.398440840890111]
  AI applications have significantly improved diagnostic accuracy, treatment personalization, and patient outcome predictions. These advancements also introduce substantial ethical and fairness challenges. These biases can lead to disparities in healthcare delivery, affecting diagnostic accuracy and treatment outcomes across different demographic groups.
  arXiv  Detail & Related papers  (2024-07-29T02:39:17Z)
• Artificial Intelligence in Industry 4.0: A Review of Integration Challenges for Industrial Systems [45.31340537171788]
  Cyber-Physical Systems (CPS) generate vast data sets that can be leveraged by Artificial Intelligence (AI) for applications including predictive maintenance and production planning. Despite the demonstrated potential of AI, its widespread adoption in sectors like manufacturing remains limited.
  arXiv  Detail & Related papers  (2024-05-28T20:54:41Z)
• Generative AI for Secure and Privacy-Preserving Mobile Crowdsensing [74.58071278710896]
  generative AI has attracted much attention from both academic and industrial fields. Secure and privacy-preserving mobile crowdsensing (SPPMCS) has been widely applied in data collection/ acquirement.
  arXiv  Detail & Related papers  (2024-05-17T04:00:58Z)
• 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)
• Generative AI for Secure Physical Layer Communications: A Survey [80.0638227807621]
  Generative Artificial Intelligence (GAI) stands at the forefront of AI innovation, demonstrating rapid advancement and unparalleled proficiency in generating diverse content. In this paper, we offer an extensive survey on the various applications of GAI in enhancing security within the physical layer of communication networks. We delve into the roles of GAI in addressing challenges of physical layer security, focusing on communication confidentiality, authentication, availability, resilience, and integrity.
  arXiv  Detail & Related papers  (2024-02-21T06:22:41Z)
• The Security and Privacy of Mobile Edge Computing: An Artificial Intelligence Perspective [64.36680481458868]
  Mobile Edge Computing (MEC) is a new computing paradigm that enables cloud computing and information technology (IT) services to be delivered at the network's edge. This paper provides a survey of security and privacy in MEC from the perspective of Artificial Intelligence (AI) We focus on new security and privacy issues, as well as potential solutions from the viewpoints of AI.
  arXiv  Detail & Related papers  (2024-01-03T07:47:22Z)
• 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)
• Risk of AI in Healthcare: A Comprehensive Literature Review and Study Framework [0.5130062125323206]
  This study conducts a thorough examination of the research stream focusing on AI risks in healthcare, aiming to explore the distinct genres within this domain. A selection criterion was employed to carefully analyze 39 articles to identify three primary genres of AI risks prevalent in healthcare: clinical data risks, technical risks, and socio-ethical risks.
  arXiv  Detail & Related papers  (2023-09-25T21:09:21Z)
• 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)
• Edge Intelligence for Empowering IoT-based Healthcare Systems [42.909808437026136]
  This article highlights the benefits of edge intelligent technology, along with AI in smart healthcare systems. A novel smart healthcare model is proposed to boost the utilization of AI and edge technology in smart healthcare systems.
  arXiv  Detail & Related papers  (2021-03-22T19:35:06Z)
• 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)
• Artificial intelligence in medicine and healthcare: a review and classification of current and near-future applications and their ethical and social Impact [0.0]
  This work is based on an analysis of the state of the art of research and technology, including existing software, personal monitoring devices, genetic tests and editing tools, personalized digital models, online platforms, augmented reality devices, and surgical and companion robotics. Motivated by our review, we present and describe the notion of 'extended personalized medicine' We study the transformations of the roles of doctors and patients in an age of ubiquitous information, identify the risk of a division of Medicine into 'fake-based', 'patient-generated', and'scientifically tailored', and draw the attention of some aspects that need further thorough analysis
  arXiv  Detail & Related papers  (2020-01-22T15:39:42Z)

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.