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Related Experiment Video

Updated: Sep 12, 2025

Using the Visual World Paradigm to Study Sentence Comprehension in Mandarin-Speaking Children with Autism

Using the Visual World Paradigm to Study Sentence Comprehension in Mandarin-Speaking Children with Autism

Published on: October 3, 2018

Exploiting Large Language Models for Diagnosing Autism Associated Language Disorders and Identifying Distinct

Chuanbo Hu¹, Wenqi Li¹, Mindi Ruan²

¹Department of Computer Science, University at Albany, Albany, 12222, NY, USA.

Research Square

|August 6, 2025

Summary

This summary is machine-generated.

Large Language Models (LLMs) improve autism diagnosis by analyzing language patterns, increasing sensitivity and predictive value. This technology aids in identifying key features for tailored treatment plans.

Keywords:

Autism spectrum disorder (ASD)Generative Pre-trained Transformer (GPT)Identified linguistic features Language deficits Large language models (LLMs)

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Area of Science:

Computational linguistics
Developmental psychology
Artificial intelligence in healthcare

Background:

Diagnosing language disorders in autism spectrum disorder (ASD) is challenging due to subjective traditional methods.
Existing assessment tools often lack the speed and precision required for timely diagnosis.

Purpose of the Study:

To explore the use of Large Language Models (LLMs) for enhancing the sensitivity and precision of autism diagnosis.
To identify and profile key linguistic features associated with ASD-related language disorders.

Main Methods:

Utilized LLMs' natural language understanding capabilities to analyze linguistic patterns in individuals with ASD.
Employed a zero-shot learning configuration to evaluate model performance against baseline methods.

Main Results:

The LLM-based approach demonstrated over a 10% increase in sensitivity and positive predictive value compared to baseline models.
Identified ten key linguistic features, including echolalia and pronoun reversal, crucial for ASD diagnosis.

Conclusions:

LLMs offer a promising, supplementary tool for improving the accuracy and efficiency of diagnosing ASD-related language patterns.
The identified linguistic features can inform the development of personalized treatment strategies for individuals with ASD.