Author

Wu-Chen Su

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Review Open access Oct 2026

Guideline-grounded large language models for extracting genome-informed clinical recommendations from electronic health records.

BACKGROUND Precision medicine requires delivery and tracking of genome-informed risk assessments (GIRAs), often documented in unstructured electronic health record (EHR) notes, making large-scale evaluation reliant on labor-intensive manual chart review. Large language models (LLMs) offer a promising approach to automa...

Yi Xin, K. W. Davis, Wu-Chen Su et al. · JAMIA Journal of the America... · 0 citations

Review Open access Aug 2026

Assessing the Reliability of LLM-Generated Phenotype-Genotype Associations Through External Validation

Background Phenotype-genotype associations underpin precision medicine by enabling disease prevention, early diagnosis, risk stratification, therapeutic target discovery, and personalized treatment. However, the rapid growth of scientific evidence has made manual curation of these associations increasingly labor-intens...

Caiwan Sun, Yi Xin, Sarah Zeng et al. · bioRxiv · 0 citations

#artificial intelligence Preprint Jun 2026

Teaching agentic AI to learn expert reasoning for rare disease diagnosis

Rare disease diagnosis depends on expert reasoning that is scarce and difficult to transfer; off-the-shelf large language models (LLMs) rank the correct disease first in only 35.4% of benchmark cases. Here we show that this expert reasoning can be converted into a scalable AI capability through a governed learning proc...

Minh-Ha Nguyen, Erica Gray, B. Schuler et al. · 0 citations