Abstract
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.
Abstract (translated)
诊断与自闭症相关的语言障碍是一个复杂而微妙的挑战，常常受到传统评估方法主观性和可变性的阻碍。传统的评估方法不仅需要大量的人力，而且通常由于其速度和准确性不足而导致延迟干预。在这项研究中，我们探讨了将 ChatGPT（一种最先进的 large language model）应用于克服这些障碍，通过提高诊断准确性和鉴定自闭症特定语言特征来提高诊断过程。利用 ChatGPT 先进自然语言处理能力，这项研究旨在简化并优化诊断过程。 具体来说，我们将 ChatGPT 的性能与包括 BERT（在各种自然语言处理任务中表现出色）在内的传统监督学习模型进行比较。我们发现 ChatGPT 远远超过了这些模型，在零散学习配置下实现了超过 13% 的准确性和 F1 分数的提高。这一显著的增强突显了该模型的潜力，作为神经诊断工具的优越性。此外，我们识别出十种与自闭症相关的语言障碍，这些障碍在不同的实验场景中具有显著的差异。这些特征（包括重复、指代词倒置和异常语言使用）对于准确诊断 ASD 和定制治疗计划至关重要。 我们在一起得出的研究结果主张，在临床环境中采用先进的 AI 工具如 ChatGPT 来评估和诊断发展障碍。我们的方法不仅承诺更高的诊断精确性，而且与个性化医疗的目标相一致，可能改变自闭症和其他神经障碍的评估格局。
URL
https://arxiv.org/abs/2405.01799
