Towards regulatory generative AI in ophthalmology healthcare: a security and privacy perspective

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.

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