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.

翻译：诊断自闭症相关的语言障碍是一项复杂且微妙的挑战，常常因传统评估方法的主观性和变异性而受阻。传统诊断方法不仅需要大量人力投入，而且由于缺乏速度和特异性，往往导致干预延迟。在本研究中，我们探索了应用当前最先进的大型语言模型ChatGPT来克服这些障碍，通过提升诊断准确性和勾勒出自闭症相关的特定语言特征。借助ChatGPT先进自然语言处理能力，本研究旨在简化和完善诊断流程。具体而言，我们比较了ChatGPT与包括BERT（一种因在多种自然语言处理任务中表现出色而备受赞誉的模型）在内的传统监督学习模型的性能。结果表明，在零样本学习配置下，ChatGPT在准确率和F1分数上均显著优于这些模型，提升幅度超过13%。这一显著增强凸显了该模型作为神经诊断领域优越工具的潜力。此外，我们识别出自闭症相关语言障碍的十个独特特征，这些特征在不同实验场景中表现出显著差异。这些特征包括回声语言、代词反转和异常语言使用，对于准确诊断自闭症谱系障碍和定制治疗方案至关重要。综合而言，我们的研究结果倡导在临床环境中采用如ChatGPT等先进AI工具来评估和诊断发育障碍。我们的方法不仅有望提高诊断精度，而且符合个性化医疗的目标，可能改变自闭症及类似神经系统疾病的评估格局。