MOSAIC: Modality-Specific Adaptation for Incremental Continual Learning in Parkinson's Disease Gait Assessment
arXiv:2606.13258v1 Announce Type: new Abstract: Gait-based Parkinson's disease assessment increasingly relies on heterogeneous sensors, but clinical systems rarely collect all modalities simultaneously. New sensors may arrive through device upgrades, protocol changes, or multi-center deployment, while historical patient data are often unavailable because of privacy and storage constraints. This modality-incremental setting faces three challenges: unreliable cross-modal distillation, modality-specific statistical shifts, and reduced plasticity after preservation. We propose MOSAIC, a compact co
The increasing reliance on heterogeneous sensors for medical assessments and challenges with data privacy and storage are driving innovation in continual learning for clinical AI systems.
This development addresses critical issues in deploying robust and adaptable AI for healthcare, particularly for progressive diseases like Parkinson's, by enabling systems to integrate new data modalities without retraining on sensitive historical information.
AI systems can now more effectively adapt to evolving data inputs from diverse medical sensors and clinical protocols while preserving patient data privacy and optimizing computational resources.
- · Healthcare AI developers
- · Patients with progressive diseases
- · Medical device manufacturers
- · Clinical research institutions
- · Legacy AI models requiring full retraining
- · Data-intensive, non-incremental AI approaches
- · Developers unprepared for diverse sensor data
Improved accuracy and adaptability of AI diagnostics in dynamic clinical environments.
Faster deployment and broader adoption of AI tools in hospitals and clinics due to lower data and computational burdens.
The acceleration of personalized medicine through AI systems capable of continuously learning from individual patient data streams.
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Read at arXiv cs.AI