Hierarchical Dual-Strategy Unlearning for Biomedical and Healthcare Intelligence Using Imperfect and Privacy-Sensitive Medical Data

Large language models (LLMs) exhibit exceptional performance across diverse domains, yet their propensity to memorise training data poses substantial privacy risks, particularly within sensitive healthcare contexts where medical data is often imperfect, insufficiently labelled, or contains privacy-sensitive patient information. Here, we present a hierarchical dual-strategy framework for selective knowledge unlearning in medical LLMs that enables precise removal of specialised knowledge whilst preserving fundamental medical competencies, specifically addressing challenges in biomedical and healthcare intelligence using imperfect data. Our approach synergistically integrates geometric-constrained gradient updates, which selectively modulate parameters encoding target knowledge while safeguarding essential capabilities, with concept-aware token-level interventions that systematically distinguish between preservation-critical and unlearning-targeted tokens through a unified four-level medical concept hierarchy. Through comprehensive evaluation on the MedMCQA dataset targeting surgical knowledge removal, and cross-domain validation on the MHQA dataset encompassing anxiety, depression, trauma, and obsessive-compulsive disorder domains, we demonstrate that our method achieves superior selective unlearning performance (82.7% forgetting rate, 88.5% knowledge preservation) compared to existing approaches. Notably, our framework maintains robust privacy guarantees while requiring modification of only 0.1% of model parameters, establishing a paradigm for privacy-preserving medical AI systems that addresses regulatory compliance, hospital auditability, and ethical imperatives in clinical research environments. This work contributes to advancing biomedical and healthcare intelligence by providing an effective solution for auditing and managing imperfect and privacy-sensitive medical data in real-world clinical applications.