# Artificial Intelligence in the Evaluation and Intervention of Developmental Coordination Disorder: A Scoping Review of Methods, Clinical Purposes, and Future Directions

## TL;DR

This review explores how artificial intelligence is being used to evaluate and treat developmental coordination disorder, finding that most efforts focus on screening and assessment rather than intervention.

## Contribution

The study provides a comprehensive overview of AI applications in DCD, highlighting a lack of advanced AI methods and intervention-focused research.

## Key findings

- AI applications in DCD are mainly focused on screening and assessment, with limited attention to intervention.
- Most studies use supervised machine learning, with few employing advanced approaches like multimodal systems or generative AI.
- Future research should prioritize AI tools that support personalized intervention and functional outcomes for DCD populations.

## Abstract

What are the main findings?
AI applications in DCD research are mainly focused on screening and assessment, with very limited attention being given to intervention.Most studies rely on supervised machine learning, while advanced approaches such as multimodal systems or generative AI are essentially absent.

AI applications in DCD research are mainly focused on screening and assessment, with very limited attention being given to intervention.

Most studies rely on supervised machine learning, while advanced approaches such as multimodal systems or generative AI are essentially absent.

What are the implications of the main findings?
AI currently supports early identification and motor assessment in DCD but is not yet widely used to enhance therapeutic intervention.Future research should prioritize clinically integrated, OT- and PT-centered AI tools to support personalized intervention and functional outcomes to populations with DCD.

AI currently supports early identification and motor assessment in DCD but is not yet widely used to enhance therapeutic intervention.

Future research should prioritize clinically integrated, OT- and PT-centered AI tools to support personalized intervention and functional outcomes to populations with DCD.

Background: Developmental coordination Disorder (DCD) is a prevalent and persistent neurodevelopmental condition characterized by motor learning difficulties that significantly affect daily functioning and participation. Despite growing interest in artificial intelligence (AI) applications within healthcare, the extent and nature of AI use in the evaluation and intervention of DCD remain unclear. Objective: This scoping review aimed to systematically map the existing literature on the use of AI and AI-assisted approaches in the evaluation, screening, monitoring, and intervention of DCD, and to identify current trends, methodological characteristics, and gaps in the evidence base. Methods: A scoping review was conducted in accordance with the PRISMA extension for Scoping Reviews (PRISMA-ScR) guidelines and was registered on the Open Science Framework. Systematic searches were performed in Scopus, PubMed, Web of Science, and IEEE Xplore, supplemented by snowballing. Peer-reviewed studies applying AI methods to DCD-relevant populations were included. Data was extracted and charted to summarize study designs, populations, AI methods, data modalities, clinical purposes, outcomes, and reported limitations. Results: Seven studies published between 2021 and 2025 met the inclusion criteria following a literature search covering the period from January 2010 to 2025. One study listed as 2026 was included based on its early access online publication in 2025. Most studies focused on AI applications for assessment, screening, and classification, using supervised machine learning or deep learning models applied to movement-based data, wearable sensors, video recordings, neurophysiological signals, or electronic health records. Only one randomized controlled trial evaluated an AI-assisted intervention. The evidence base was dominated by early-phase development and validation studies, with limited external validation, heterogeneous diagnostic definitions, and scarce intervention-focused research. Conclusions: Current AI research in DCD is primarily centered on evaluation and early identification, with comparatively limited evidence supporting AI-assisted intervention or rehabilitation. While existing findings suggest that AI has the potential to enhance objectivity and sensitivity in DCD assessment, significant gaps remain in clinical translation, intervention development, and implementation. Future research should prioritize theory-informed, clinician-centered AI applications, including adaptive intervention systems and decision-support tools, to better support occupational therapy and physiotherapy practice in DCD care.