EDITORIAL: The Silent Burden of Communication Disorders: Artificial Intelligence for Transforming Recognition, Informed Decisions and Outcomes

Abstract

Communication is a fundamental human right and the cornerstone of learning, employment, social participation and overall well-being. Communication disorders including speech, language, voice, fluency, hearing, and cognitive abilities, affect hundreds of millions of people worldwide. The World Health Organization (WHO) reported that more than 430 million people have hearing loss that is incapacitating, while millions more experience acquired and developmental communication disorders associated with autism spectrum disorder, stroke, traumatic brain injury, Parkinson's disease, dementia and other neurological conditions.

Due to a lack of epidemiological data, underreporting, insufficient screening programs and a shortage of speech-language pathologists the actual prevalence in Pakistan is still unknown. Consequently, many people remain undiagnosed or only receive intervention after the most effective period for recovery has been lost. Communication disorders represent a silent burden, making them one of the most overlooked health challenges. In contrast to physical disabilities, communication difficulties such as problems with speaking, understanding, reading, writing, or social communication often go unrecognized which can result in delayed diagnosis, poor academic performance, unemployment, social isolation, psychological distress, and a lower quality of life. The nature of these impairments can make early recognition difficult, complicating clinical assessment and delaying informed decision-making. 

Artificial intelligence (AI) offers an unprecedented opportunity to make invisible burden visible by identifying early or subtle changes in speech language, and communication abilities that may not be readily observed during traditional clinical assessment. Progress in speech analytics, natural language processing, machine learning, and digital biomarkers can generate objective data to supplement traditional evaluation methods and support more accurate and timely clinical decision making................................