Consumer AI health assistants increasingly connect medical records, patient reports, and wearable data across repeated interactions, but evaluation still focuses on isolated prompts and answers. This Perspective has two objectives: to define a journey-level evaluation and governance framework for record-linked patien...
Zonghai Yao, Hong Yu· npj Digital Medicine· 0 citations
Large language model systems can retrieve and cite medical evidence, yet their outputs may remain difficult to review during care. We define reviewability through evidence adequacy for action-changing claims, exact source verification, and presentation matched to a specified reviewer and decision point. We propose a...
Zonghai Yao, Hong Yu· npj Digital Medicine· 0 citations
MultiViewDx, a partly physician-validated multimodal instruction dataset for evidence-linked multi-view medical imaging diagnosis, is introduced and Controlled ablations and clinician evaluation show that both case-level multi-view organization and evidence-linked reasoning targets contribute to the gain.
Jun-Da Wang, Zonghai Yao, Yujan Ting et al.· Transactions of the Associat...· 2 citations· ⚡1
Across six medical multimodal MCQ datasets, this work separate candidate cues from behavioral evidence through prompt- and image-side audits, modality ablations, and matched repairs that preserve the medical target and answer key, showing that medical image-reasoning claims require route-level evidence.
Ben Wang, Yi-Fan Zhang, Jia-Qing Yu et al.· 0 citations
This study evaluated both closed-source and open-source large language models for extracting and prioritizing medical jargon from EHR notes relevant to individual patients, leveraging prompting techniques, fine-tuning, and data augmentation and found that model performance could deviate largely based on prompting style...
W. Jang, Sharmin Sultana, Zonghai Yao et al.· JMIR AI· 1 citation
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