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

WHY IT MATTERS

If this AI tool works well, patients with rare diseases could get the right genetic test recommended faster, potentially leading to quicker diagnoses and treatment decisions.

Researchers are testing whether artificial intelligence programs called large language models can help doctors decide which genetic tests to order for patients with rare diseases. Instead of doctors having to memorize complicated guidelines, the AI could read the patient's information and recommend whether a simple gene panel or a more complete genetic test would be best. This could make the process faster and more consistent across different hospitals.

Interpretable Fine-tuned Large Language Models Facilitate Making Genetic Test Decisions for Rare Diseases Authors: Nguyen, Q. M. et al. Server: medRxiv Category: health informatics Abstract: 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 face interpretability limitations. We hypothesize that large language models (LLMs) can be fine-tuned