Speech, language, and communication symptoms enable the early detection,
diagnosis, treatment planning, and monitoring of neurocognitive disease
progression. Nevertheless, traditional manual neurologic assessment, the speech
and language evaluation standard, is time-consuming and resource-intensive for
clinicians. We argue that Computational Language Assessment (C.L.A.) is an
improvement over conventional manual neurological assessment. Using machine
learning, natural language processing, and signal processing, C.L.A. provides a
neuro-cognitive evaluation of speech, language, and communication in elderly
and high-risk individuals for dementia. ii. facilitates the diagnosis,
prognosis, and therapy efficacy in at-risk and language-impaired populations;
and iii. allows easier extensibility to assess patients from a wide range of
languages. Also, C.L.A. employs Artificial Intelligence models to inform theory
on the relationship between language symptoms and their neural bases. It
significantly advances our ability to optimize the prevention and treatment of
elderly individuals with communication disorders, allowing them to age
gracefully with social engagement.Comment: 36 pages, 2 figures, to be submite