xRead - Incorporating Artificial Intelligence into Clinical Practice (March 2026)

Language Processing of Reddit Posts?,” American Journal of Audiology 31 (2022): 993–1002, https://​doi.​org/​10.​1044/​2021_​AJA-​21-​00158​. 139. J. R. Lechien, M. R. Naunheim, A. Maniaci, et al., “Performance and Consistency of ChatGPT-4 Versus Otolaryngologists: A Clinical Case Series,” Otolaryngology and Head and Neck Surgery 170, no. 6 (2024): 1519–1526, https://​doi.​org/​10.​1002/​ohn.​759. 140. L. Revercomb, A. M. Patel, H. S. Choudhry, and A. Filimonov, “Performance of ChatGPT in Otolaryngology Knowledge Assessment,” American Journal of Otolaryngology 45, no. 1 (2024): 104082, https://​doi.​ org/​10.​1016/j.​amjoto.​2023.​104082. 141. M. Noda, T. Ueno, R. Koshu, et al., “Performance of GPT-4V in Answering the Japanese Otolaryngology Board Certification Examination Questions: Evaluation Study,” JMIR Medical Education 10 (2024): e57054, https://​doi.​org/​10.​2196/​57054​. 142. S. Nofal, J. Niu, P. Resong, et al., “Personal History of Cancer as a Risk Factor for Second Primary Lung Cancer: Implications for Lung Cancer Screening,” Cancer Medicine 13, no. 5 (2024): e7069, https://​doi.​ org/​10.​1002/​cam4.​7069. 143. S. Meksawasdichai, T. Lerksuthirat, B. Ongphiphadhanakul, and C. Sriphrapradang, “Perspectives and Experiences of Patients With Thyroid Cancer at a Global Level: Retrospective Descriptive Study of Twitter Data,” JMIR Cancer 9 (2023): e48786, https://​doi.​org/​10.​2196/​ 48786​. 144. “Predicting Health-Related Quality of Life Change Using Natural Language Processing in Thyroid Cancer-PubMed,” accessed December 22, 2024, https://​pubmed.​ncbi.​nlm.​nih.​gov/​37664​403/​. 145. T. Drake, A. Gravely, A. Westanmo, and C. Billington, “Prevalence of Thyroid Incidentalomas From 1995 to 2016: A Single-Center, Retrospective Cohort Study,” Journal of the Endocrine Society 4, no. 1 (2020): bvz027, https://​doi.​org/​10.​1210/​jendso/​bvz027. 146. J. R. Bellinger, M. W. Kwak, G. A. Ramos, J. S. Mella, and J. L. Mattos, “Quantitative Comparison of Chatbots on Common Rhinology Pathologies,” Laryngoscope 134, no. 10 (2024): 4225–4231, https://​doi.​ org/​10.​1002/​lary.​31470​. 147. H. A. Jung, O. Jeong, D. K. Chang, et al., “Real-Time autOmati cally Updated Data warehOuse in healThcare (ROOT): An Innovative and Automated Data Collection System,” Translational Lung Cancer Research 10, no. 10 (2021): 3865–3874, https://​doi.​org/​10.​21037/​ tlcr-​21-​531. 148. A. Lorenzi, G. Pugliese, A. Maniaci, et al., “Reliability of Large Language Models for Advanced Head and Neck Malignancies Management: A Comparison Between ChatGPT 4 and Gemini Advanced,” European Archives of Oto-Rhino-Laryngology 281, no. 9 (2024): 5001–5006, https://​doi.​org/​10.​1007/​s0040​5-​024-​08746​-​2. 149. A. M. Saibene, F. Allevi, C. Calvo-Henriquez, et al., “Reliability of Large Language Models in Managing Odontogenic Sinusitis Clinical Scenarios: A Preliminary Multidisciplinary Evaluation,” European Archives of Oto-Rhino-Laryngology 281, no. 4 (2024): 1835–1841, https://​ doi.​org/​10.​1007/​s0040​5-​023-​08372​-​4. 150. C. P. Cheng, R. Sicard, D. Vujovic, et al., “Replicating Current Procedural Terminology Code Assignment of Rhinology Operative Notes Using Machine Learning,” World Journal of Otorhinolaryngology— Head and Neck Surgery , (2024), https://​doi.​org/​10.​1002/​wjo2.​188. 151. C.-G. Cheng, D.-C. Wu, J.-C. Lu, et al., “Restricted Use of Copy and Paste in Electronic Health Records Potentially Improves Healthcare Quality,” Medicine 101, no. 4 (2022): e28644, https://​doi.​org/​10.​1097/​ MD.​00000​00000​028644. 152. M. Sievert, O. Conrad, S. K. Mueller, et al., “Risk Stratification of Thyroid Nodules: Assessing the Suitability of ChatGPT for Text-Based Analysis,” American Journal of Otolaryngology 45, no. 2 (2024): 104144, https://​doi.​org/​10.​1016/j.​amjoto.​2023.​104144.

153. J. L. Bader and M. F. Theofanos, “Searching for Cancer Information on the Internet: Analyzing Natural-Language Search Queries,” Journal of Medical Internet Research 5, no. 4 (2003): 80–108. 154. R. Murphy Lonergan, J. Curry, K. Dhas, and B. I. Simmons, “Stratified Evaluation of GPT'S Question Answering in Surgery Reveals Artificial Intelligence (AI) Knowledge Gaps,” Cureus 15, no. 11 (2023): e48788, https://​doi.​org/​10.​7759/​cureus.​48788​. 155. X. Huang, H. Chen, and J. D. Yan, “Study on Structured Method of Chinese MRI Report of Nasopharyngeal Carcinoma,” BMC Medical Informatics and Decision Making 21, no. Suppl 2 (2021): 203, https://​doi.​ org/​10.​1186/​s1291​1-​021-​01547​-​1. 156. F. P. Y. Lin, A. Pokorny, C. Teng, and R. J. Epstein, “TEPAPA: A Novel In Silico Feature Learning Pipeline for Mining Prognostic and Associative Factors From Text-Based Electronic Medical Records,” Scientific Reports 7, no. 1 (2017): 6918, https://​doi.​org/​10.​1038/​s4159​8-​017-​07111​-​0. 157. “Testing and Validation of a Custom Retrained Large Language Model for the Supportive Care of HN Patients with External Knowledge Base,”, accessed December 22, 2024, https://​www.​mdpi.​com/​2072-​ 6694/​16/​13/​2311. 158. A. Warrier, R. Singh, A. Haleem, H. Zaki, and J. A. Eloy, “The Comparative Diagnostic Capability of Large Language Models in Otolaryngology,” Laryngoscope 134, no. 9 (2024): 3997–4002, https://​ doi.​org/​10.​1002/​lary.​31434​. 159. M. Abou-Abdallah, T. Dar, Y. Mahmudzade, J. Michaels, R. Talwar, and C. Tornari, “The Quality and Readability of Patient Information Provided by ChatGPT: Can AI Reliably Explain Common ENT Operations?,” European Archives of Oto-Rhino-Laryngology 281, no. 11 (2024): 6147–6153, https://​doi.​org/​10.​1007/​s0040​5-​024-​08598​-​w. 160. E. A. Patel, L. Fleischer, P. Filip, et al., “The Use of Artificial Intelligence to Improve Readability of Otolaryngology Patient Education Materials,” Otolaryngology and Head and Neck Surgery 171, no. 2 (2024): 603–608, https://​doi.​org/​10.​1002/​ohn.​816. 161. C. J. Washington, M. Abouyared, S. Karanth, et al., “The Use of Chatbots in Head and Neck Mucosal Malignancy Treatment Recommendations,” Otolaryngology and Head and Neck Surgery 171, no. 4 (2024): 1062–1068, https://​doi.​org/​10.​1002/​ohn.​818. 162. A. Różańska, E. Gliwska, K. Barańska, S. Maćkowska, A. Sobol, and D. Spinczyk, “The Use of Natural Language Processing Elements for Computer-Aided Diagnostics and Monitoring of Body Image Perception in Enterally Fed Patients With Head and Neck or Upper Gastrointestinal Tract Cancers,” Cancers 16, no. 7 (2024): 1353, https://​ doi.​org/​10.​3390/​cance​rs160​71353​. 163. E. Gliwska, K. Baranska, S. Mackowska, A. Rozanska, A. Sobol, and D. Spinczyk, “The Use of Natural Language Processing for Computer-Aided Diagnostics and Monitoring of Body Image Perception in Patients With Cancers,” Cancers 15, no. 22 (2023): 5437, https://​doi.​ org/​10.​3390/​cance​rs152​25437​. 164. C. S. Lam, K. Zhou, H. H. F. Loong, V. C. H. Chung, C. K. Ngan, and Y. T. Cheung, “The Use of Traditional, Complementary, and Integrative Medicine in Cancer: Data-Mining Study of 1 Million Web-Based Posts From Health Forums and Social Media Platforms,” Journal of Medical Internet Research 25 (2023): e45408, https://​doi.​org/​10.​2196/​45408​. 165. S. Dhar, D. Kothari, M. Vasquez, et al., “The Utility and Accuracy of ChatGPT in Providing Post-Operative Instructions Following Tonsillectomy: A Pilot Study,” International Journal of Pediatric Otorhinolaryngology 179 (2024): 111901, https://​doi.​org/​10.​1016/j.​ijporl.​ 2024.​111901. 166. D. R. Grimm, Y. J. Lee, K. Hu, et al., “The Utility of ChatGPT as a Generative Medical Translator,” European Archives of Oto-Rhino-­ Laryngology 281, no. 11 (2024): 6161–6165, https://​doi.​org/​10.​1007/​ s0040​5-​024-​08708​-​8.

3062

The Laryngoscope, 2025

Made with FlippingBook - professional solution for displaying marketing and sales documents online