Navigating Unlearning in Medical AI: A Framework for Diabetic Retinopathy Classification

Xinghao Li, Chi Liu, Youyang Qu, Shujie Cui, Cunjian Chen, Xingliang Yuan, Zongyuan Ge, Longxiang Gao

Abstract: Medical Artificial Intelligence (AI) systems hold great potential for clinical and diagnostic applications. However, their deployment raises significant privacy and ethical concerns, primarily due to the reliance on large-scale datasets containing sensitive health information. The processes of collecting, storing, and using patient data bring forth widespread concerns about privacy protection and data ownership. Additionally, the dynamic nature of medical data presents risks related to outdated or inaccurate information, which can directly impact patient care. Introducing the concept of “unlearning” in medical AI is therefore crucial, not only to address patient privacy needs but also to ensure that models remain adaptable to evolving medical knowledge and data quality. Nevertheless, implementing unlearning in medical AI is considerably more complex compared to other domains, as errors in this context could lead to severe consequences for patient health. This paper aims to establish an evaluation framework specifically for unlearning in classification tasks on the Mobile Brazilian Retinal Dataset (mBRSET) in medical AI. The framework addresses key issues such as privacy, clinical safety, diagnostic accuracy, and fairness. By providing a detailed foundation for assessing unlearning practices, the framework is tailored to the unique challenges presented by the mBRSET dataset. Ultimately, this work seeks to identify the trade-offs and requirements necessary to implement responsible unlearning processes in this highly sensitive domain, thereby contributing to the safe and ethical advancement of medical AI.

External IDs:doi:10.1109/tetci.2025.3641713