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

Archives of Oto-Rhino-Laryngology 281, no. 4 (2024): 2081–2086, https://​ doi.​org/​10.​1007/​s0040​5-​023-​08104​-​8. 112. P. Chen, C. Feng, L. Huang, H. Chen, Y. Feng, and S. Chang, “Exploring the Research Landscape of the Past, Present, and Future of Thyroid Nodules,” Frontiers in Medicine 9 (2022): 831346, https://​doi.​ org/​10.​3389/​fmed.​2022.​831346. 113. J. C. Lee, C. S. Hamill, Y. Shnayder, E. Buczek, K. Kakarala, and A. M. Bur, “Exploring the Role of Artificial Intelligence Chatbots in Preoperative Counseling for Head and Neck Cancer Surgery,” Laryngoscope 134, no. 6 (2024): 2757–2761, https://​doi.​org/​10.​1002/​lary.​ 31243​. 114. F. Teixeira-Marques, N. Medeiros, F. Nazaré, et al., “Exploring the Role of ChatGPT in Clinical Decision-Making in Otorhinolaryngology: A ChatGPT Designed Study,” European Archives of Oto-Rhino-­ Laryngology 281, no. 4 (2024): 2023–2030, https://​doi.​org/​10.​1007/​s0040​ 5-​024-​08498​-​z. 115. A. Pathak, Z. Yu, D. Paredes, et al., “Extracting Thyroid Nodules Characteristics From Ultrasound Reports Using Transformer-Based Natural Language Processing Methods,” AMIA Annual Symposium Proceedings (2023): 1193–1200. 116. M. Noda, H. Yoshimura, T. Okubo, et al., “Feasibility of Multimodal Artificial Intelligence Using GPT-4 Vision for the Classification of Middle Ear Disease: Qualitative Study and Validation,” JMIR AI 3 (2024): e58342, https://​doi.​org/​10.​2196/​58342​. 117. T. K. Colicchio, P. I. Dissanayake, and J. J. Cimino, “Formal Representation of Patients' Care Context Data: The Path to Improving the Electronic Health Record,” Journal of the American Medical Informatics Association 27, no. 11 (2020): 1648–1657, https://​doi.​org/​10.​ 1093/​jamia/​ocaa134. 118. J. L. Farlow, M. Abouyared, E. M. Rettig, A. Kejner, R. Patel, and H. A. Edwards, “Gender Bias in Artificial Intelligence-Written Letters of Reference,” Otolaryngology and Head and Neck Surgery 171, no. 4 (2024): 1027–1032, https://​doi.​org/​10.​1002/​ohn.​806. 119. V. Vasan, C. P. Cheng, C. J. Fan, et al., “Gender Differences in Letters of Recommendations and Personal Statements for Neurotology Fellowship Over 10Years: A Deep Learning Linguistic Analysis,” Otology & Neurotology 45, no. 8 (2024): 827–832, https://​doi.​org/​10.​ 1097/​MAO.​00000​00000​004265. 120. V. Vasan, C. P. Cheng, S. Edalati, et al., “Gender-Based Linguistic Differences in Letters of Recommendation for Rhinology Fellowship Over Time: A Dual-Institutional Follow-Up Study Using Natural Language Processing and Deep Learning,” International Forum of Allergy & Rhinology 14, no. 11 (2024): 1814–1817, https://​doi.​org/​10.​ 1002/​alr.​23411​. 121. V. Kunz, V. Wildfeuer, R. Bieck, et al., “Keyword-Augmented and Semi-Automatic Generation of FESS Reports: A Proof-Of-Concept Study,” International Journal of Computer Assisted Radiology and Surgery 18, no. 5 (2023): 961–968, https://​doi.​org/​10.​1007/​s1154​8-​022-​ 02791​-​0. 122. Y. Yoshiyasu, F. Wu, A. K. Dhanda, D. Gorelik, M. Takashima, and O. G. Ahmed, “GPT-4 Accuracy and Completeness Against International Consensus Statement on Allergy and Rhinology: Rhinosinusitis,” International Forum of Allergy & Rhinology 13, no. 12 (2023): 2231–2234, https://​doi.​org/​10.​1002/​alr.​23201​. 123. Y. Hassona, D. Alqaisi, A. AL-Haddad, et al., “How Good Is ChatGPT at Answering Patients' Questions Related to Early Detection of Oral (Mouth) Cancer?,” Oral Surgery, Oral Medicine, Oral Pathology and Oral Radiology 138, no. 2 (2024): 269–278, https://​doi.​org/​10.​1016/j.​ oooo.​2024.​04.​010. 124. A. Karlsson, A. Ellonen, H. Irjala, et al., “Impact of Deep Learning-­ Determined Smoking Status on Mortality of Cancer Patients: Never Too Late to Quit,” ESMO Open 6, no. 3 (2021): 100175, https://​doi.​org/​10.​ 1016/j.​esmoop.​2021.​100175.

125. Q. Zhang, S. Zhang, J. Li, et al., “Improved Diagnosis of Thyroid Cancer Aided With Deep Learning Applied to Sonographic Text Reports: A Retrospective, Multi-Cohort, Diagnostic Study,” Cancer Biology & Medicine 19, no. 5 (2021): 733–741, https://​doi.​org/​10.​20892/​ j.​issn.​2095-​3941.​2020.​0509. 126. A. D. Oliva, L. J. Pasick, M. E. Hoffer, and D. E. Rosow, “Improving Readability and Comprehension Levels of Otolaryngology Patient Education Materials Using ChatGPT,” American Journal of Otolaryngology 45, no. 6 (2024): 104502, https://​doi.​org/​10.​1016/j.​am joto.​2024.​104502. 127. V. Dallari, A. Sacchetto, R. Saetti, L. Calabrese, F. Vittadello, and L. Gazzini, “Is Artificial Intelligence Ready to Replace Specialist Doctors Entirely? ENT Specialists vs ChatGPT: 1–0, Ball at the Center,” European Archives of Oto-Rhino-Laryngology 281, no. 2 (2023): 995– 1023, https://​doi.​org/​10.​1007/​s0040​5-​023-​08321​-​1. 128. O. Kuscu, A. E. Pamuk, N. Sutay Suslu, and S. Hosal, “Is ChatGPT Accurate and Reliable in Answering Questions Regarding Head and Neck Cancer?,” Frontiers in Oncology 13 (2023): 1256459, https://​doi.​ org/​10.​3389/​fonc.​2023.​1256459. 129. J. Patel, P. Robinson, E. Illing, and B. Anthony, “Is ChatGPT 3.5 Smarter Than Otolaryngology Trainees? A Comparison Study of Board Style Exam Questions,” PLoS One 19, no. 9 (2024): e0306233, https://​doi.​ org/​10.​1371/​journ​al.​pone.​0306233. 130. J. R. Lechien, A. Gorton, J. Robertson, and L. A. Vaira, “Is ChatGPT-4 Accurate in Proofread a Manuscript in Otolaryngology-­ Head and Neck Surgery?,” Otolaryngology 170, no. 6 (2024): 1527–1530, https://​doi.​org/​10.​1002/​ohn.​526. 131. Y. Hu, G. Wen, J. Ma, et al., “Label-Indicator Morpheme Growth on LSTM for Chinese Healthcare Question Department Classification,” Journal of Biomedical Informatics 82 (2018): 154–168, https://​doi.​org/​10.​ 1016/j.​jbi.​2018.​04.​011. 132. A. S. Halagur, K. Balakrishnan, and N. Ayoub, “Large Language Models in Otolaryngology Residency Admissions: A Random Sampling Analysis,” Laryngoscope 135, no. 1 (2025): 87–93, https://​doi.​org/​10.​ 1002/​lary.​31705​. 133. V. Vasan, C. Cheng, D. K. Lerner, et al., “Letters of Recommendations and Personal Statements for Rhinology Fellowship: A Deep Learning Linguistic Analysis,” International Forum of Allergy & Rhinology 13, no. 10 (2023): 1971–1973, https://​doi.​org/​10.​1002/​alr.​23153​. 134. V. Vasan, C. P. Cheng, D. K. Lerner, et al., “Machine Learning for Predictive Analysis of Otolaryngology Residency Letters of Recommendation,” Laryngoscope 134, no. 9 (2024): 4016–4022, https://​ doi.​org/​10.​1002/​lary.​31439​. 135. R. G. Short, S. Dondlinger, and B. Wildman-Tobriner, “Management of Incidental Thyroid Nodules on Chest CT: Using Natural Language Processing to Assess White Paper Adherence and Track Patient Outcomes,” Academic Radiology 29, no. 3 (2022): e18–e24, https://​doi.​ org/​10.​1016/j.​acra.​2021.​02.​019. 136. J. Zhang, M. A. Mazurowski, B. C. Allen, and B. Wildman-­ Tobriner, “Multistep Automated Data Labelling Procedure (MADLaP) for Thyroid Nodules on Ultrasound: An Artificial Intelligence Approach for Automating Image Annotation,” Artificial Intelligence in Medicine 141 (2023): 102553, https://​doi.​org/​10.​1016/j.​artmed.​2023.​102553. 137. J. Zeng, I. Banerjee, A. S. Henry, et al., “Natural Language Processing to Identify Cancer Treatments With Electronic Medical Records,” JCO Clinical Cancer Informatics 5 (Zeng) Department of Management Science and Engineering, Huang Engineering Center, Stanford, CA, United States(Banerjee) Department of Biomedical Informatics, Department of Radiology, Emory University School of Medicine, Atlanta, GA, United States(Henry) (2021): 379–389, https://​ doi.​org/​10.​1200/​CCI.​20.​00173​. 138. V. Manchaiah, A. Londero, A. K. Deshpande, et al., “Online Discussions About Tinnitus: What Can we Learn From Natural

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