  Speech, language, and communication deficits are present in most
neurodegenerative syndromes. They enable the early detection, diagnosis,
treatment planning, and monitoring of neurocognitive disease progression as
part of traditional neurological assessment. Nevertheless, standard speech and
language evaluation is time-consuming and resource-intensive for clinicians. We
argue that using machine learning methodologies, natural language processing,
and modern artificial intelligence (AI) for Language Assessment is an
improvement over conventional manual assessment. Using these methodologies,
Computational Language Assessment (CLA) accomplishes three goals: (i) 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. By employing AI models, CLA may inform neurocognitive theory on the
relationship between language symptoms and their neural bases. Finally, it
signals a paradigm shift by significantly advancing our ability to optimize the
prevention and treatment of elderly individuals with communication disorders,
allowing them to age gracefully with social engagement.
