Challenges in translating AI-driven ASD/ADHD diagnosis: A methodological systematic review.

Abstract

Early and accurate diagnosis of neurodevelopmental disorders (NDDs), including autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD), remains a critical challenge in pediatric care. Traditional methods rely on subjective behavioral assessments that are time-intensive and prone to bias. This systematic review synthesizes biomedical informatics methodologies using deep learning-driven computer vision to enable objective, data-driven diagnostic decision support for pediatric NDDs. Following PRISMA guidelines, we searched Web of Science and Scopus (2020-2024), identifying 43 Q1/Q2 studies. Four informatics-focused research questions were addressed: multimodal feature extraction, deep learning architectures, high-performing strategies, and robust data integration challenges. Methodological quality and bias were assessed using the APPRAISE-AI framework. Multimodal fusion and hybrid informatics pipelines dominated (38% of studies), outperforming unimodal approaches by integrating complementary streams-facial imaging (high specificity), EEG/fMRI (superior sensitivity). Transfer learning and fusion techniques were prevalent, but federated learning and explainable AI were underutilized. APPRAISE-AI revealed strong clinical relevance (72.8%) and reporting quality (66.1%), yet substantial gaps in reproducibility (41.0%) and result robustness (45.1%). AI-driven biomedical informatics holds significant potential to reduce diagnostic delays and costs in NDDs. However, reproducibility, interpretability, and ethical data integration must be improved through standardized, privacy-preserving, and auditable frameworks to enable scalable clinical deployment.