International Archives of Otorhinolaryngology, Table of Contents CC BY 4.0 · Int Arch Otorhinolaryngol 2025; 29(01): s00441801781DOI: 10.1055/s-0044-1801781 Editorial Artificial Intelligence for Diagnosis and Treatment of Dysphagia Authors Author Affiliations Geraldo Pereira Jotz 1 Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre, Brazil 2 Universidade do Vale do Sinos (UNISINOS), Brazil 3 Voice Institute, Porto Alegre, Brazil Arthur Viana Jotz 4 Pontifícia Universidade Católica do Rio Grande do Sul (PUCRS), Brazil Daniel Arnold 1 Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre, Brazil Wyllians Vendramini Borelli 1 Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre, Brazil 5 Memory Center, Hospital Moinhos de Vento (HMV), Brazil Recommend Article Abstract Bibliographical RecordGeraldo Pereira Jotz, Arthur Viana Jotz, Daniel Arnold, Wyllians Vendramini Borelli. Artificial Intelligence for Diagnosis and Treatment of Dysphagia. Int Arch Otorhinolaryngol 2025; 29: s00441801781. DOI: 10.1055/s-0044-1801781 Full Text References References 1 Heo S, Uhm KE, Yuk D. et al. Deep learning approach for dysphagia detection by syllable-based speech analysis with daily conversations. Sci Rep 2024; 14 (01) 20270 2 Kim JM, Kim MS, Choi SY, Ryu JS. Prediction of dysphagia aspiration through machine learning-based analysis of patients' postprandial voices. J Neuroeng Rehabil 2024; 21 (01) 43 3 Jauk S, Kramer D, Veeranki SPK. et al. Evaluation of a Machine Learning-Based Dysphagia Prediction Tool in Clinical Routine: A Prospective Observational Cohort Study. Dysphagia 2023; 38 (04) 1238-1246 4 Kim JM, Kim MS, Choi SY, Lee K, Ryu JS. A deep learning approach to dysphagia-aspiration detecting algorithm through pre- and post-swallowing voice changes. Front Bioeng Biotechnol 2024; 12: 1433087 5 Khalifa Y, Donohue C, Coyle JL, Sejdic E. Autonomous Swallow Segment Extraction Using Deep Learning in Neck-Sensor Vibratory Signals From Patients With Dysphagia. IEEE J Biomed Health Inform 2023; 27 (02) 956-967 6 Weng W, Imaizumi M, Murono S, Zhu X. Expert-level aspiration and penetration detection during flexible endoscopic evaluation of swallowing with artificial intelligence-assisted diagnosis. Sci Rep 2022; 12 (01) 21689 7 Jeong CW, Lee CS, Lim DW. et al. The Development of an Artificial Intelligence Video Analysis-Based Web Application to Diagnose Oropharyngeal Dysphagia: A Pilot Study. Brain Sci 2024; 14 (06) 546 [Internet] 8 Saab R, Balachandar A, Mahdi H. et al. Machine-learning assisted swallowing assessment: a deep learning-based quality improvement tool to screen for post-stroke dysphagia. Front Neurosci 2023; 17: 1302132 9 Gugatschka M, Egger NM, Haspl K. et al. Clinical evaluation of a machine learning-based dysphagia risk prediction tool. Eur Arch Otorhinolaryngol 2024; 281 (08) 4379-4384