Dementia diagnosis requires a series of different testing methods, which is
complex and time-consuming. Early detection of dementia is crucial as it can
prevent further deterioration of the condition. This paper utilizes a speech
recognition model to construct a dementia assessment system tailored for
Mandarin speakers during the picture description task. By training an
attention-based speech recognition model on voice data closely resembling
real-world scenarios, we have significantly enhanced the model's recognition
capabilities. Subsequently, we extracted the encoder from the speech
recognition model and added a linear layer for dementia assessment. We
collected Mandarin speech data from 99 subjects and acquired their clinical
assessments from a local hospital. We achieved an accuracy of 92.04% in
Alzheimer's disease detection and a mean absolute error of 9% in clinical
dementia rating score prediction.

通过训练一个基于注意力的语音识别模型并在精心设计的图片描述任务上使用，本研究构建了一个适用于普通话使用者的痴呆评估系统，通过提取语音识别模型的编码器并添加一个线性层用于痴呆评估，从 99 名被试中收集普通话语音数据并获取他们的临床评估，成功实现了 92.04% 的阿尔茨海默病检测准确率和 9% 的临床痴呆评分的平均绝对误差。

基于注意力的语音识别编码器的普通话言语痴呆评估

Dementia Assessment Using Mandarin Speech with an Attention-based Speech  Recognition Encoder

Recent investigations show that large language models (LLMs), specifically
GPT-4, not only have remarkable capabilities in common Natural Language
Processing (NLP) tasks but also exhibit human-level performance on various
professional and academic benchmarks. However, whether GPT-4 can be directly
used in practical applications and replace traditional artificial intelligence
(AI) tools in specialized domains requires further experimental validation. In
this paper, we explore the potential of LLMs such as GPT-4 to outperform
traditional AI tools in dementia diagnosis. Comprehensive comparisons between
GPT-4 and traditional AI tools are conducted to examine their diagnostic
accuracy in a clinical setting. Experimental results on two real clinical
datasets show that, although LLMs like GPT-4 demonstrate potential for future
advancements in dementia diagnosis, they currently do not surpass the
performance of traditional AI tools. The interpretability and faithfulness of
GPT-4 are also evaluated by comparison with real doctors. We discuss the
limitations of GPT-4 in its current state and propose future research
directions to enhance GPT-4 in dementia diagnosis.

本研究探讨了大型语言模型（LLM），特别是 GPT-4，在痴呆症诊断中的潜力和局限，通过对两个真实临床数据集的实验结果表明，尽管 LLMs 具有未来进步的潜力，但目前在诊断准确性方面仍不及传统 AI 工具。

LLM （如 GPT-4）是否能在痴呆症诊断中胜过传统人工智能工具？也许，但今天还没有

Can LLMs like GPT-4 outperform traditional AI tools in dementia  diagnosis? Maybe, but not today

In recent years there has been a burgeoning interest in the use of
computational methods to distinguish between elicited speech samples produced
by patients with dementia, and those from healthy controls. The difference
between perplexity estimates from two neural language models (LMs) - one
trained on transcripts of speech produced by healthy participants and the other
trained on transcripts from patients with dementia - as a single feature for
diagnostic classification of unseen transcripts has been shown to produce
state-of-the-art performance. However, little is known about why this approach
is effective, and on account of the lack of case/control matching in the most
widely-used evaluation set of transcripts (DementiaBank), it is unclear if
these approaches are truly diagnostic, or are sensitive to other variables. In
this paper, we interrogate neural LMs trained on participants with and without
dementia using synthetic narratives previously developed to simulate
progressive semantic dementia by manipulating lexical frequency. We find that
perplexity of neural LMs is strongly and differentially associated with lexical
frequency, and that a mixture model resulting from interpolating control and
dementia LMs improves upon the current state-of-the-art for models trained on
transcript text exclusively.

通过混合训练有痴呆症和健康参与者语言的神经语言模型，使用单一特征的若干计算机方法进行痴呆症患者诊断分类，从而实现聚类的最新方法。