Dr Anna Girardi1,2, Prof Elizabeth Cardell1,2,3, Prof Stephen Bird1,2
1School of Health and Medical Sciences, University of Southern Queensland, Ipswich, Australia, 2Centre for Health Research, University of Southern Queensland, Toowoomba, Australia, 3School of Medicine and Dentistry, Griffith University, Gold Coast, Australia
Biography:
Dr. Anna Girardi (PhD, MSpPathSt, PGDipPsych, BA(Psych), ATCL (London), CPSP) is a Lecturer and Practice Education Coordinator for Speech Pathology at UniSQ. She completed her PhD at The University of Queensland, focusing on the impact of burn injuries, including button battery ingestions, on paediatric swallowing. With over a decade of clinical experience, Anna specialises in dysphagia management and researches artificial intelligence applications in swallowing and voice assessment, as well as dysphagia in psychiatric populations. A passionate advocate for button battery safety, she is regularly featured in the media.
Abstract:
Artificial Intelligence (AI) is emerging as a transformative force in healthcare, with the potential to significantly empower clinicians and improve patient outcomes. In speech pathology, AI can revolutionise the interpretation of videofluoroscopic swallow studies (VFSS), an important diagnostic imaging tool in swallowing assessments. This narrative review resulted in the identification of eleven key studies that explore how AI, particularly deep learning (DL) models, can enhance the efficiency, accuracy, and accessibility of VFSS interpretation, ultimately reshaping the approach to dysphagia diagnostics and care. The included studies showcase how AI models are being applied to improve the precision of swallowing assessments, providing insights into how these technologies can be integrated into clinical practice to support speech pathologists in making more informed and timely decisions. AI-driven approaches to VFSS interpretation have demonstrated significant benefits, including improved diagnostic accuracy, faster processing, and the ability to detect subtle swallowing impairments that may otherwise be missed. These innovations empower speech pathologists by providing enhanced diagnostic tools, particularly in underserved areas where access to specialised expertise may be limited. The result is more individualised, efficient, and timely interventions, which improve the quality of care and patient outcomes. Integrating AI into VFSS interpretation can empower speech pathologists, and other health professionals, to provide more precise, evidence-based care, ultimately improving patient outcomes. However, challenges such as data requirements, system transparency, and biases must be addressed. Collaboration and further validation are key to realising AI’s full potential in reshaping swallowing diagnostics and advancing healthcare.