Papers by Kyle Lam

1 papers
MEDSYN: Benchmarking Multi-EviDence SYNthesis in Complex Clinical Cases for Multimodal Large Language Models (2026.findings-acl)

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Challenge: Existing benchmarks for multimodal large language models do not capture real-world clinical complexity.
Approach: They evaluate multilingual, multimodal multimodal models of clinical cases with up to 7 distinct visual clinical evidence types per case.
Outcome: The proposed model outperforms human models on differential diagnosis (DDx) generation and final diagnosis (FDx) selection.

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