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Interpretable Fine-tuned Large Language Models Facilitate Making Genetic Test Decisions for Rare Diseases

2026-03-02 health informatics Title + abstract only
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Clinical decision making often relies on expert judgment guided by established guidelines, which can be challenging to standardize and abstract to implement. For example, selecting between gene panels and whole exome/genome sequencing (WES/WGS) for rare disease diagnosis frequently requires interpretation of evidence-based recommendations from the American College of Medical Genetics and Genomics (ACMG) guideline. Traditional machine learning (ML) models predicting suitable genetic tests often f...

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