Background and Aim: Although artificial intelligence (AI) has increasingly been integrated into health and rehabilitation sciences, the assessment domains in which AI-based approaches are applied in pediatric occupational therapy have not yet been comprehensively mapped in the literature. The aim of this study was to systematically identify the assessment domains in which AI-based approaches have been applied in pediatric occupational therapy and to describe how these applications have been reported in the existing literature.
Materials and Methods: A scoping review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews. Searches were performed in CINAHL, OTseeker, PubMed/MEDLINE, Scopus, and Web of Science for English-language, peer-reviewed studies published between 2015 and 2025. Empirical studies reporting the use of AI-based approaches for assessment purposes in pediatric occupational therapy were included. Data were charted and synthesized descriptively to provide an overview of research characteristics and thematic patterns.
Results: Sixteen studies met the inclusion criteria. Most studies employed observational or validation-based designs and primarily utilized machine learning approaches, including deep learning and computer vision techniques. AI-based assessment applications were predominantly focused on motor and sensory-perceptual domains. Fewer studies addressed cognitive functioning and activities of daily living, while no studies explicitly examined psychosocial, environmental, or participation-related assessment domains. Image- and video-based data were the most frequently used modalities.
Conclusion: The findings indicate that AI-based assessment research in pediatric occupational therapy has largely concentrated on performance-oriented domains, particularly motor and sensory-perceptual functioning. Important occupational therapy domains, such as participation, environmental context, and psychosocial functioning, remain underrepresented. This scoping review provides an overview of current research trends and highlights key gaps, offering a foundation to guide future interdisciplinary research and the development of more holistic, occupation-centered AI-based assessment approaches within health sciences.