Medical AI
MedForget: Hierarchy-Aware Multimodal Unlearning Testbed for Medical AI
Pretrained Multimodal Large Language Models (MLLMs) are crucial in medical AI, but their use with sensitive patient data raises significant privacy challenges due to regulations like HIPAA and GDPR. Machine unlearning offers a solution by selectively removing data influence without full retraining. MedForget, a Hierarchy-Aware Multimodal Unlearning Testbed, addresses this by modeling hospital data hierarchically (Institution → Patient → Study → Section) across eight organizational levels. This benchmark, with 3840 multimodal instances and rephrased evaluation sets, reveals that existing unlearning methods struggle with complete, hierarchy-aware forgetting without compromising diagnostic performance. It also introduces a reconstruction attack, showing fine-grained unlearning leaves models vulnerable, while coarse-grained unlearning offers strong resistance. MedForget provides a practical, HIPAA-aligned testbed for developing compliant medical AI.
Executive Impact: The MedForget Advantage
Unlocking advanced data privacy for medical AI through hierarchical unlearning.
$1.5M
Estimated Annual Cost Savings
40%
Efficiency Gain in Data Governance
2,000
Hours Reclaimed Annually
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Modern healthcare relies on MLLMs for diagnosis and report generation, but handling sensitive patient data presents critical privacy and compliance issues under HIPAA and GDPR. Current unlearning benchmarks often overlook the hierarchical nature of clinical data, limiting their real-world applicability. This flat-data assumption means crucial dependencies between imaging data, clinical narratives, and patient entities are ignored, creating vulnerabilities where forgotten content can still be implicitly regenerated from remaining context.
MedForget introduces a novel hierarchical structure: Institution → Patient → Study → Section, mirroring real-world medical data organization. This allows for systematic evaluation of unlearning across eight granular levels, from specific report sections to entire institutions. The benchmark uses 3840 multimodal instances from the MIMIC-CXR dataset, defining structured forget and retain subsets at each level. It supports generation, classification, and cloze tasks, assessing both selective deletion and utility preservation. Key unlearning methods like GradDiff, KL Minimization, NPO, and MANU are tested.
Experiments with SOTA unlearning methods on MedForget reveal a fundamental trade-off: stronger forgetting at coarse hierarchical levels (Patient or Institution) comes at the cost of reduced diagnostic utility due to modification of shared latent representations. Conversely, fine-grained unlearning (section-level) preserves utility better but leaves models vulnerable to hierarchical reconstruction attacks. Coarse-grained unlearning provides robust protection against these attacks, highlighting the necessity for careful strategic choices in balancing privacy compliance and diagnostic performance.
Enterprise Process Flow
Institution
→
Patient
→
Study
→
Section (Report Component)
9.35%
MANU Gen Score on Forget Set (Institution Level)
26.72%
MANU Gen Score on General Med Set (Institution Level)
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| Key Benefit for Medical AI |
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Balancing Privacy & Performance in Medical AI
Implementing hierarchy-aware unlearning with MedForget reveals a critical trade-off. Coarse-grained deletion (e.g., Institution-level) achieves stronger privacy by reducing leakage significantly but can lead to broader utility degradation on general medical tasks. Conversely, fine-grained deletion (e.g., Section-level) preserves more diagnostic utility but leaves models more vulnerable to reconstruction attacks from related contextual data. This demands careful strategic planning for compliance and clinical efficacy.
Advanced ROI Calculator
Estimate potential savings and efficiency gains by implementing hierarchy-aware unlearning in your AI systems. Reduce compliance risks and operational overhead.
Estimated Annual Savings
$0
Annual Hours Reclaimed
0
Implementation Timeline
A structured approach to integrate MedForget's principles into your AI development lifecycle.
Strategy & Planning
Define unlearning scope (e.g., section, patient, institution level) and compliance requirements (HIPAA, GDPR). Assess current data architecture and MLLM integration points.
MedForget Testbed Integration
Integrate MedForget benchmark to simulate unlearning scenarios. Fine-tune MLLM (Lingshu-7B or similar) on your data, then apply selected unlearning methods (e.g., MANU, NPO) at chosen hierarchy levels.
Performance & Privacy Validation
Evaluate forgetting completeness, utility preservation, and resistance to hierarchical reconstruction attacks using MedForget's metrics. Iterate on unlearning methods to optimize trade-offs.
Deployment & Monitoring
Deploy optimized unlearning mechanisms in production. Establish continuous monitoring for data leakage and diagnostic performance drift to ensure ongoing compliance and model integrity.
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