Advances in current state of diagnostic approaches towards investigating pervasive development disorder

Abstract

Pervasive development disorder (PDD) is characterized by various types of neurodevelopment conditions influencing communication skill, behavioral, and social skills. Current state of screening methods mainly uses neurophysiological and clinical assessments that are reliable but suffer from challenges e.g., scalability, increased cost, and subjectivity. Artificial intelligence (AI) based solution is witnessed to have increased adoption in this regard also suffers from clinical integration, data bias, and explainability-oriented problems despite its potential contribution till date. Therefore, the proposed study presents a systematic review of recent literature with a closer emphasis on AI and non-AI-based methodologies adopted to diagnose PDD. The study outcome also exhibits similar facts. The novelty of this work resides in its comparative analysis, temporal mapping of current trends, and its integrative framework. It also contributes towards identifying certain underexplored areas, thereby facilitating practically viable insight towards the future direction of implementation. The idea is to understand its viability, which is not only clinically relevant but also offers sustainable diagnostics.