Assessing Language Disorders using Artificial Intelligence: a Paradigm Shift
This work addresses the challenge of improving clinical efficiency and accessibility in language disorder assessment for patients with neurodegenerative syndromes, representing a paradigm shift in the field.
The paper tackles the problem of time-consuming and resource-intensive manual assessment of language disorders in neurodegenerative diseases by proposing Computational Language Assessment (CLA) using AI, which enables early detection, diagnosis, and monitoring of neurocognitive progression in elderly and at-risk populations.
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.