CODA: Cohort- and Drift-aware Foundation Model for Multimodal Clinical Reasoning

Key points

• Recent advances in multimodal foundation models have demonstrated strong performance across diverse clinical tasks.1
• However, existing approaches predominantly operate at the individual sample level and fail to model the interplay between population-level cohort patterns and patient-specific variation that underlie real-world clinical reasoning.1
• CODA explicitly integrates cohort structure into the modeling pipeline through two complementary mechanisms: cohort-aware population stratification and drift-aware individual adaptation.1
• In particular, we leverage reinforcement learning to discover clinically meaningful patient cohorts, and further model each patient's deviation from cohort prototypes as a structured drift signal encoding fine-grained individual heterogeneity.1

Sources (1)

• [1]CODA: Cohort- and Drift-aware Foundation Model for Multimodal Clinical Reasoning
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 19, 06:29 AM

  Motivated by this gap, we propose CODA, a cohort- and drift-aware foundation model for multimodal clinical reasoning.

  Recent advances in multimodal foundation models have demonstrated strong performance across diverse clinical tasks.

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