Privacy-Preserving Multimodal AI for MS Prognosis

Overview

This research develops a privacy-preserving multimodal large language model system for personalized multiple-sclerosis lesion-activity prognosis. The system combines clinical narratives and MRI while keeping model inference local.

Research direction

• A retrieval-synthesis-critique workflow assembles grounded evidence for the final assessment.
• The design emphasizes privacy, traceability, and cautious use in a clinical decision-support setting.
• Current work studies systematic failure subgroups through multimodal decision embeddings.

This project is conducted at Mila - Quebec AI Institute under the supervision of Professor Tal Arbel.