A Systematic Review of Explainable Multimodal Artificial Intelligence for Early Identification of Developmental Potential in Children

Abstract

Children who are developmentally lagging require quick and tailored supports- this is why focusing on potential development is important. AI has advanced by integrating multiple forms of speech, behaviour, physiology, and vision, and emerging AI systems for speech triage and multi-modal systems are demonstrating promise for comprehensive pediatric assessment. This literature review examines the most recent XAI approaches and applications to developmental screening and assessment of learning and development from 2016-2025. Under the PRISMA model, 34 studies were selected that involve data fusion, explainable AI, and stakeholder-driven explainable AI. The review explores various AI model approaches, data fusion, explainable AI, and evaluation with explainable AI trust, ethical, bias, and generalization frameworks. Survey results of the various frameworks show that XAI approaches and selected strikingly high attribution approaches of clinician educator explanation dominated stakeholder-controlled assessment and understanding and maintained high diagnostic and predictive accuracy levels. The issues of availability and sustaining longitudinal studies and accountability for cultural and demographic bias are persistent. This review suggests discussing adaptive and transparent AI systems that are equitable, scalable, and reliable. This synthesis aids the creation of efficient and useful AI technologies for fair access to early childhood assessment and intervention tools.