Application of intelligent technologies for dysphagia risk prediction: A scoping review

Objective: To systematically review the current state of research on the use of intelligent technologies for dysphagia risk prediction. This scoping review summarizes existing studies in terms of their technical approaches, data sources, and model performance, and analyzes key technological limitations and challenges in clinical translation. The goal is to provide insights for future study design and clinical implementation. Methods: This study was developed based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) checklist to comply with reporting and methodological standards. We systematically searched eight databases and clinical trial registries from their inception until February 25, 2026. Studies that underwent peer review as well as relevant grey literature were included. Subsequently, the results were comprehensively analyzed and discussed. Results: A total of 38 studies were ultimately included, most of which were single-center retrospective cohort studies with sample sizes ranging from 50 to 59,811 participants. The number of publications has grown rapidly since 2023. The study populations mainly included patients with stroke, head and neck cancer patients undergoing radiotherapy, and older adults. Structured clinical data were the predominant data source, while only a few studies incorporated multimodal inputs such as imaging, physiological, or acoustic signals. Traditional machine learning models were most commonly used, followed by traditional statistical models, hybrid models, and deep learning methods. Overall model performance was generally good (area under the curve values mostly exceeding 0.70), yet there was considerable heterogeneity in model types, data sources, and outcome reporting. Conclusion: Intelligent technologies show promising potential in dysphagia risk prediction. However, existing evidence remains heterogeneous and primarily exploratory. Future research should strengthen the construction of multicenter datasets, multimodal data integration, and external validation to enhance model generalizability and clinical applicability.