Parkinsons disease is the second most prevalent neurodegenerative disorder
with over ten million active cases worldwide and one million new diagnoses per
year. Detecting and subsequently diagnosing the disease is challenging because
of symptom heterogeneity with respect to complexity, as well as the type and
timing of phenotypic manifestations. Typically, language impairment can present
in the prodromal phase and precede motor symptoms suggesting that a
linguistic-based approach could serve as a diagnostic method for incipient
Parkinsons disease. Additionally, improved linguistic models may enhance other
approaches through ensemble techniques. The field of large language models is
advancing rapidly, presenting the opportunity to explore the use of these new
models for detecting Parkinsons disease and to improve on current linguistic
approaches with high-dimensional representations of linguistics. We evaluate
the application of state-of-the-art large language models to detect Parkinsons
disease automatically from spontaneous speech with up to 73% accuracy.

利用大型语言模型，通过自发语音进行自动检测帕金森病的精确度高达 73%。