Speech, language, and communication deficits are a common symptom in several neurodegenerative conditions. These deficits are often among the first symptoms to emerge, which makes them particularly important for early detection, diagnosis, and treatment planning. Traditional neurological assessments routinely include an evaluation of these functions as they provide critical insights into the progression of neurocognitive diseases. However, traditional speech and language evaluations pose significant challenges for clinicians as they are time-consuming and require substantial resources. In this context, we argue that the use of machine learning methodologies, natural language processing (NLP), and modern artificial intelligence (AI) for Language Assessment represents a significant improvement over conventional manual assessment methods. Open Brain AI aims to help clinicians and researchers employ CLA in their everyday practices.

Computational Language Assessment (CLA) leverages these advanced technologies to accomplish three primary goals. First, it enables a comprehensive neuro-cognitive evaluation of speech, language, and communication in elderly and high-risk individuals for dementia. This is crucial as early detection of dementia and other neurocognitive diseases is essential for timely intervention and better patient outcomes. Second, CLA facilitates the diagnosis, prognosis, and assessment of therapy efficacy in at-risk and language-impaired populations. This helps in personalizing treatment plans and monitoring progress over time. Third, it allows easier extensibility to assess patients from a wide range of languages. This is particularly important in a globalized world where patients may speak a variety of languages that are not always comprehensively covered by traditional assessment tools.

By employing advanced AI models, CLA can also provide valuable insights into the neurocognitive theory on the relationship between language symptoms and their neural bases. This can help in understanding the underlying mechanisms of language and communication deficits in neurodegenerative diseases and inform the development of more effective interventions.

Finally, CLA significantly advances our ability to optimize the prevention and treatment of elderly individuals with communication disorders. This is of utmost importance as communication is a key aspect of social engagement, which in turn is associated with better cognitive and emotional well-being. By improving the assessment and management of communication disorders, CLA can help elderly individuals maintain better social engagement, thereby contributing to healthier aging.