Article Text

Download PDFPDF
Towards regulatory generative AI in ophthalmology healthcare: a security and privacy perspective
  1. Yueye Wang1,
  2. Chi Liu2,
  3. Keyao Zhou3,4,
  4. Tianqing Zhu2,
  5. Xiaotong Han1
  1. 1Sun Yat-sen University Zhongshan Ophthalmic Center State Key Laboratory of Ophthalmology, Guangzhou, Guangdong, China
  2. 2Faculty of Data Science, City University of Macau, Macao SAR, China
  3. 3Department of Ophthalmology, Guangdong Provincial People's Hospital, Guangzhou, Guangdong, China
  4. 4Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, China
  1. Correspondence to Dr Xiaotong Han, Sun Yat-Sen University Zhongshan Ophthalmic Center State Key Laboratory of Ophthalmology, Guangzhou, Guangdong, China; lh.201205{at}aliyun.com; Dr Chi Liu, Faculty of Data Science, City University of Macau, Macao SAR, China; chiliu{at}cityu.edu.mo

Abstract

As the healthcare community increasingly harnesses the power of generative artificial intelligence (AI), critical issues of security, privacy and regulation take centre stage. In this paper, we explore the security and privacy risks of generative AI from model-level and data-level perspectives. Moreover, we elucidate the potential consequences and case studies within the domain of ophthalmology. Model-level risks include knowledge leakage from the model and model safety under AI-specific attacks, while data-level risks involve unauthorised data collection and data accuracy concerns. Within the healthcare context, these risks can bear severe consequences, encompassing potential breaches of sensitive information, violating privacy rights and threats to patient safety. This paper not only highlights these challenges but also elucidates governance-driven solutions that adhere to AI and healthcare regulations. We advocate for preparedness against potential threats, call for transparency enhancements and underscore the necessity of clinical validation before real-world implementation. The objective of security and privacy improvement in generative AI warrants emphasising the role of ophthalmologists and other healthcare providers, and the timely introduction of comprehensive regulations.

  • Public health

Data availability statement

Data sharing not applicable as no data sets generated and/or analysed for this study. Not applicable.

Statistics from Altmetric.com

Request Permissions

If you wish to reuse any or all of this article please use the link below which will take you to the Copyright Clearance Center’s RightsLink service. You will be able to get a quick price and instant permission to reuse the content in many different ways.

Data availability statement

Data sharing not applicable as no data sets generated and/or analysed for this study. Not applicable.

View Full Text

Footnotes

  • YW and CL are joint first authors.

  • Contributors YW and CL developed the concept of the manuscript. YW, CL and KZ drafted the manuscript. XH and TZ made critical revisions of the manuscript. XH and TZ supervised the entire work. All authors approved the completed version. We used ChatGPT-4 only for proofreading and grammar checking of this review, but have not relied on the tool for any thinking or material presentation in this paper.

  • Funding This study was funded by the National Natural Science Foundation of China (82000901, 82101171), the Global STEM Professorship Scheme (P0046113), the Fundamental Research Funds of the State Key Laboratory of Ophthalmology (303060202400201209), the Outstanding PI Research Funds of the State Key Laboratory of Ophthalmology (3030902113083).

  • Competing interests None declared.

  • Provenance and peer review Not commissioned; externally peer reviewed.