Exploring the Integration of Machine Learning and AI in the Treatment and Diagnosis of Communication Disorders

Communication disorders affect a significant portion of the population, and conventional diagnostic and treatment methods encounter challenges related to accessibility and efficiency. This paper examines how machine learning (ML) and artificial intelligence (AI) can be integrated into the diagnosis and treatment of communication disorders. The study emphasizes the potential transformation brought about by these technologies in improving patient outcomes, specifically highlighting successful initial diagnosis rates achieved with ML classifiers. Various novel ML methods developed over the years are discussed, offering the prospect of more precise treatment options for individuals with cognitive disabilities. However, the current research has limitations, including a narrow focus, the absence of a common gold standard, and difficulties in maintaining consistency across studies. Ethical considerations play a crucial role in the discussion, particularly concerning data privacy, security, and potential biases in ML models. The lack of diverse and representative datasets is noted as a factor that may lead to disparities in care. To advance the field, it is essential to replicate results across larger cohorts, address technical challenges, and improve the interpretability and transparency of AI-generated recommendations. These steps are identified as crucial for the ongoing development of ML and AI applications in the diagnosis and treatment of communication disorders.