Luis Filipe Nakayama — accepted AI-conference papers

2025

Multi-OphthaLingua: A Multilingual Benchmark for Assessing and Debiasing LLM Ophthalmological QA in LMICs

David Restrepo, Chenwei Wu, Zhengxu Tang, Zitao Shuai, Thao Nguyen Minh Phan, Jun-En Ding +11

AAAI 2025technical

Current ophthalmology clinical workflows are plagued by over-referrals, long waits, and complex and heterogeneous medical records. Large language models (LLMs) present a promising solution to automate various procedures such as triaging, preliminary tests like visual acuity assessment, and report su…

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2025

WorldMedQA-V: a multilingual, multimodal medical examination dataset for multimodal language models evaluation

João Matos, Shan Chen, Siena Kathleen V. Placino, Yingya Li, Juan Carlos Climent Pardo, Daphna Idan +10

NAACL 2025findings

Multimodal/vision language models (VLMs) are increasingly being deployed in healthcare settings worldwide, necessitating robust benchmarks to ensure their safety, efficacy, and fairness. Multiple-choice question and answer (QA) datasets derived from national medical examinations have long served as…

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