  Diagnosing language disorders associated with autism is a complex and nuanced
challenge, often hindered by the subjective nature and variability of
traditional assessment methods. Traditional diagnostic methods not only require
intensive human effort but also often result in delayed interventions due to
their lack of speed and specificity. In this study, we explored the application
of ChatGPT, a state of the art large language model, to overcome these
obstacles by enhancing diagnostic accuracy and profiling specific linguistic
features indicative of autism. Leveraging ChatGPT advanced natural language
processing capabilities, this research aims to streamline and refine the
diagnostic process. Specifically, we compared ChatGPT's performance with that
of conventional supervised learning models, including BERT, a model acclaimed
for its effectiveness in various natural language processing tasks. We showed
that ChatGPT substantially outperformed these models, achieving over 13%
improvement in both accuracy and F1 score in a zero shot learning
configuration. This marked enhancement highlights the model potential as a
superior tool for neurological diagnostics. Additionally, we identified ten
distinct features of autism associated language disorders that vary
significantly across different experimental scenarios. These features, which
included echolalia, pronoun reversal, and atypical language usage, were crucial
for accurately diagnosing ASD and customizing treatment plans. Together, our
findings advocate for adopting sophisticated AI tools like ChatGPT in clinical
settings to assess and diagnose developmental disorders. Our approach not only
promises greater diagnostic precision but also aligns with the goals of
personalized medicine, potentially transforming the evaluation landscape for
autism and similar neurological conditions.
