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August 22, 2025JAMA Network OpenOpen Access

Large Language Models for Rare Disease Diagnosis at the Undiagnosed Diseases Network

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Large language models can accurately identify diagnoses for rare diseases in patients referred to the Undiagnosed Diseases Network, improving diagnosis rates.
The study assessed clinical information of patients from the Undiagnosed Diseases Network, revealing that LLMs can enhance diagnostic accuracy.
Cohort study analyzing patient data demonstrates the ability of LLMs to assist in achieving diagnoses for complex cases.
Results support the integration of LLMs in clinical settings, suggesting benefits for diagnosing rare diseases and reducing diagnostic odyssey.

Implication

Cohort study shows large language models can support diagnosis in rare disease cases, highlighting potential benefits for patient care.

Abstract

This cohort study assesses whether large language models (LLMs) can identify the final diagnosis, based on available clinical information, for patients referred to the Undiagnosed Diseases Network.

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Cite This Study

Shyr et al. (2025) studied this question.

synapsesocial.com/papers/68af5407ad7bf08b1eadaf5e https://doi.org/10.1001/jamanetworkopen.2025.28538

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Implication

Cohort study shows large language models can support diagnosis in rare disease cases, highlighting potential benefits for patient care.

Abstract

This cohort study assesses whether large language models (LLMs) can identify the final diagnosis, based on available clinical information, for patients referred to the Undiagnosed Diseases Network.

Large Language Models for Rare Disease Diagnosis at the Undiagnosed Diseases Network

Key Points

Implication

Abstract

Discussion

Cite This Study

Large Language Models for Rare Disease Diagnosis at the Undiagnosed Diseases Network

Key Points

Implication

Abstract

Discussion

Cite This Study

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