The Technological Advances in Artificial Intelligence-assisted Diagnosis of Dysphagia: A Mini Review.

BACKGROUND

Dysphagia is a common complaint that afflicts the elderly and individuals with neurological disorders. Conventional diagnostic techniques, such as Videofluoroscopic Swallowing Studies (VFSS) and Fiberoptic Endoscopic Evaluation of Swallowing (FEES), have disadvantages including inter-rater variability, radiation exposure, invasive procedures, the need for clinician observation, and subjectivity. The review aims to highlight applications of Artificial Intelligence (AI) using Deep Learning (DL) and machine-learning methods to diagnose dysphagia, with the objective of increasing diagnostic accuracy, objectivity, and specificity.

METHODS

A literature review was performed using the following databases: PubMed, Scopus, Web of Science, and Google Scholar, covering the period from January 2011 to December 2025. These articles were selected based on the use of AI for dysphagia Evaluation and the reporting of diagnostic metrics.

RESULTS

The AI models, such as wearable sensor technologies and Inflated 3D Convolutional Neural Networks, achieved higher classification accuracy of 95.96% and success detection of 97.5% for the swallowing reflex class than conventional diagnostic modalities.

DISCUSSION: The other side of AI model incorporation attenuates the role of traditional diagnostic methods by enabling continuous monitoring of objective device data. However, even among more technologically advanced methods, the constraints and complexity of AI applications pose a barrier to calls for sex-specific model calibration.

CONCLUSION

Artificial Intelligence represents a dramatic advancement in dysphagia diagnosis, offering greater accuracy, diagnostic objectivity and real-time analysis. Nonetheless, realising their full potential requires coordinated efforts to incorpórate these computational techniques into standard clinical bedside practice.