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.

本研究探讨了 ChatGPT 作为一种先进的自然语言处理模型在诊断自闭症相关语言障碍方面的应用，通过提高诊断准确性和描述自闭症的特定语言特征，从而优化诊断流程，并与传统的监督学习模型进行了对比，结果显示 ChatGPT 在零样本学习配置下的准确性和 F1 分数均比其他模型提高 13% 以上，展现了其在神经学诊断中的潜力。同时，研究还找到了与自闭症相关的十个不同实验场景下显著变化的语言特征，例如回声言语、代词转位和非典型语言使用等，这些特征对于准确诊断自闭症和定制治疗方案具有重要意义。因此，该研究呼吁在临床实践中采用像 ChatGPT 这样的先进人工智能工具来评估和诊断发育性障碍，为自闭症和类似神经疾病的评估提供更高准确性，并与个体化医疗的目标相一致，从而可以潜在地改变诊断的格局。

利用 ChatGPT 诊断与自闭症相关的语言障碍并识别独特特征

Exploiting ChatGPT for Diagnosing Autism-Associated Language Disorders  and Identifying Distinct Features

Dementia is associated with language disorders which impede communication.
Here, we automatically learn linguistic disorder patterns by making use of a
moderately-sized pre-trained language model and forcing it to focus on
reformulated natural language processing (NLP) tasks and associated linguistic
patterns. Our experiments show that NLP tasks that encapsulate contextual
information and enhance the gradient signal with linguistic patterns benefit
performance. We then use the probability estimates from the best model to
construct digital linguistic markers measuring the overall quality in
communication and the intensity of a variety of language disorders. We
investigate how the digital markers characterize dementia speech from a
longitudinal perspective. We find that our proposed communication marker is
able to robustly and reliably characterize the language of people with
dementia, outperforming existing linguistic approaches; and shows external
validity via significant correlation with clinical markers of behaviour.
Finally, our proposed linguistic disorder markers provide useful insights into
gradual language impairment associated with disease progression.

通过自动学习语言模型和强化自然语言处理任务，我们构建了数字化语言标记，可以测量沟通质量和各种语言障碍的强度，并揭示了与病情进展相关的逐渐语言损害的有用见解。