AI-DRIVEN DIAGNOSTIC SYSTEMS FOR EARLY IDENTIFICATION OF OCD, AUTISM, AND DEMENTIA

Abstract

Artificial Intelligence (AI) has become one of the potent methods in enhancing the early diagnosis of neurocognitive and behavioral disorders. This paper examines AI-based diagnostic systems in Obsessive-Compulsive Disorder (OCD), Autism Spectrum Disorder (ASD) and Dementia. The conditions are normally diagnosed late as they have similar symptoms and require subjective clinical evaluation. Machine learning, deep learning, natural language processing, and computer vision are AI methods that can be used to perform analysis of complex data, such as neuroimaging, speech, behavioral data, and electronic health records. The results show that AI improves diagnostic accuracy, minimizes delays and helps to detect the disease at the earliest stage. Multimodal AI solutions also enhance predictive performance and allow customized healthcare. Nevertheless, such challenges as small datasets, transparency, and ethical issues still exist. Various datasets, longitudinal research, and explainable AI models are also identified in the study as the necessary factors to enhance clinical adoption and reliability.