Clawson, M.G. A contemporary review of Otolaryngology machine learning – head and neck surgery. Laryngoscope 130, 45–51 (2020).
bur, am, shew, M. &new, J. Artificial Intelligence for Otolaryngologists: A cutting-edge review. Otolaryngol. Neck surgery. 160, 603–611 (2019).
Liu, GS, Jovanovic, N., Sung, CK & Doyle, Scoping review of artificial intelligence detection in PC speech pathology: challenges and opportunities. Otolaryngol. Neck surgery. 171, 658–666 (2024).
Artificial intelligence and machine learning (AI/ML) compatible medical devices. FDA https://www.fda.gov/medical-devices/software-medical-device-samd/artificial-intelligence-and-machine-reading-aiml-enabled-medical-devices (2024).
ENT navigation application using BrainLab’s KickEm. BrainLab https://www.brainlab.com/surgery-products/overview-ent-products/ent-navigation-application/.
Rethinking how medical devices are designed. PacificMD Biotech https://www.pmdbiotech.com/.
Lu, J.H. et al. Assessment of adherence to reporting guidelines with commonly used clinical prediction models from a single vendor: a systematic review. Jamanet. Open 5, E2227779 (2022).
Vasey, B. Etal. Reporting guidelines for early stage clinical evaluation of artificial intelligence-driven decision support systems: decision-AI. nut. Pharmaceuticals. 28, 924–933 (2022).
Gajecki, T., Zhang, Y. & Nogueira, W. Deep removal sound coding strategy for ch cow implants. IEEE Transformer. Biomed. Eng. 70, 2700–2709 (2023).
Raghavan, Am, Lipschitz, N., Breen, J.T., Samy, RN & Kohlberg, GD visual speech recognition: Improved noise speech recognition with artificial intelligence. Otolaryngol. -head. Neck surgery. J. Am. Academy. Otolaryngol. -head. Neck surgery. 163, 771–777 (2020).
Healy, E. W., Taherian, H., Johnson, Em & Wang, D. J. Acoust. Soc. morning. 150, 3976 (2021).
Cohen, JF, et al. Guidelines for reporting Stard 2015 diagnostic accuracy studies: explanation and details. BMJ Open 6, E012799 (2016).
Transparent report (tripod) of multivariable predictive models for individual prognosis or diagnosis of Collins, GS, Reitsma, JB, Altman, DG & Moons, Kg Transparent Report (Tripod): Tripod Statement. BMC Med. 13, 1 (2015).
Des Jarlais, DC, Lyles, C. & Crepaz, N. morning. J. Public Health 94, 361–366 (2004).
von Elm, E. et al. Enhanced reporting of observational studies in epidemiology (STROBE) statements: Guidelines for reporting observational studies. BMJ 335, 806–808 (2007).
Liu, X., Cruz Rivera, S., Moher, D., Calvert, MJ & Denniston, AK Reporting Guidelines Guidelines for Clinical Trial Reporting for Interventions Including Artificial Intelligence: Consort-AI Expansion. nut. Pharmaceuticals. 26, 1364–1374 (2020).
Liu, Y., Chen, P.-H.C., Krause, J. & Peng, L. How to read articles using machine learning: A guide to medical literature users. JAMA 322, 1806–1816 (2019).
Servaraj, RR, et al. Grad-Cam: Visual description from deep networks with gradient-based localization. int. J. Comput. Vis. 128, 336–359 (2020).
Zhou, B., Khosla, A., Lapedriza, A., Oliva, A. & Torralba, A. 2016 Conference on IEEE Computer Vision and Pattern Recognition (CVPR) 2921–2929 (IEEE, 2016). https://doi.org/10.1109/cvpr.2016.319.
Kamran, F. Etal. Evaluation of sepsis prediction models before treatment onset. NEJM AI 1, AIOA2300032 (2024).
Ruamviboonsuk, P. Etal. Screening for real-time diabetic retinopathy with deep learning in a multisite national screening program: a prospective intervention cohort study. Lancet Digit. Health 4, E235 – E244 (2022).
Lind Plesner, L. Etal. A commercially available chest radiograph AI tool for detecting air disease, pneumothorax and pleural effusion. Radiology 308, E231236 (2023).
Gomez Rossi, J., Rojas-Perilla, N., Krois, J. & Schwendicke, F. Cost-effectiveness of artificial intelligence as a decision support system applied to the detection and gradient of melanoma, tooth decay and diabetic retinopathy. Jamanet. Open 5, E220269 (2022).
Seneviratne, M.G., Shah, N.H. & Chu, L. BMJ Innov. 6, 45–47 (2020).
Mennella, C., Maniscalco, U., De Pietro, G. & Esposito, M. Ethical and regulatory challenges of AI technology in healthcare: a narrative review. Heliyon 10, E26297 (2024).
Crowson, MG & Rameau, A. A report on standardized machine learning manuscripts for ENT head and neck surgery. Laryngoscope 132, 1698–1700 (2022).
Callahan, A. Etal. FURM Standing on the Ground: A framework for evaluating fair, useful and reliable AI models in healthcare systems. NEJM Katar. Innovation. Care Delivery. 5, https://doi.org/10.1056/cat.24.013 (2024).
Liu, GS et al. Deep learning classification of malignant transformation of inverse papilloma using 3D convolutional neural networks and magnetic resonance imaging. int. Forum allergy. 12, 1025–1033 (2022).
Liu, GS et al. Elhnet: A convolutional neural network for classifying intra-intima implant high drops imaged by optical coherence tomography. Biomed. Opt. Express 8, 4579–4594 (2017).
Liu, GS, Shenson, JA, Farrell, JE & Blevins, NH signaling and noise ratio quantifies the contribution of spectral channels to the classification of human head and neck tissues in vivo using deep learning and multispectral imaging. J. Biomed. Opt. 28, 016004 (2023).
Shenson, J.A., Liu, G.S., Farrell, J. & Blevins, Multispectral Imaging for Automatic Tissue Identification of NH Normal Human Surgery Specimens. Otolaryngol. head. Neck surgery. J. Am. Academy. Otolaryngol. head. Neck surgery. 164, 328–335 (2021).
Liu, GS et al. End-to-end deep learning classification of vocal pathology using stacked vowels. Laryngoscopic investigation. Otolaryngol. 8, 1312–1318 (2023).
Liu, GS, Cooperman, SP, Neves, Ca & Blevins, Estimation of Cochlear implant insertion depth using 2D-3D enrollment of NH postoperative x-rays and preoperative CT images. Otool. Neurothol. 45, E156 (2024).
Neves, Ca et al. Automatic radioactive analysis of vestibular schwanoma and inner ear using contrast-enhanced T1-weighted and T2-weighted magnetic resonance imaging sequences and artificial intelligence. Otool. Neurothol. 44, E602 (2023).
Liu, GS et al. Artificial intelligence tracking of otology instruments in a lactation video. Otool. Neurothol. 45, 1192 (2024).
Youssef, A. Etal. External validation of AI models in health should be replaced with iterations of local validation. nut. Pharmaceuticals. 29, 2686–2687 (2023).
Granlund, T., Stirbu, V. & Mikkonen, T. To MLOPS compliant in regulatory order: Oravizio’s journey from machine learning experiments to certified medical products deployed. SN Comput. SCI. 2, 342 (2021).
Beswick, DM et al. Design and theoretical future multicenter registry for patients with sinus malignancies. laryngoscope 126, 1977–1980 (2016).
Clawson, M.G. A systematic review of the federated learning application of biomedical data. PLOS digits. Health 1, E0000033 (2022).
Warnat-Herrethal, S. et al. Flock learning for distributed and confidential clinical machine learning. Nature 594, 265–270 (2021).
Holm, EA defends the Black Box. Science 364, 26–27 (2019).
Ghassemi, M., Oakden-Rayner, L. & false hopes of current approaches to explainable artificial intelligence in Beam, Al, healthcare. Lancet Digit. Health 3, E745 – E750 (2021).
Ayoub, N.F., Lee, Y.-J., Grimm, D. & Balakrishnan, K. Comparison of ChatGPT and Google search as a source of postoperative patient instructions. Jama Otolaryngol. Neck surgery. 149, 556–558 (2023).
Vaira, La et al. Validating quality analysis of medical artificial intelligence (QAMAI) tools: A new tool for assessing the quality of health information provided by AI platforms. EUR. arch. otorhinolaryngol. 281, 6123–6131 (2024).
Vaira, La et al. Enhanced AI chatbot responses in healthcare: Smart prompt structure in head and neck surgery. OTO Open 9, E70075 (2025).
Judges, CS, etc. Multimodal artificial intelligence in medicine. Kidney 360 5, 1771 (2024).
Frosolini, A. Etal. Artificial Intelligence in Audiology: A Scoping Review of Current Applications and Future Directions. Sensor 24, 7126 (2024).
Tricco, AC et al. Prisma Extensions for Scoping Reviews (Prisma-SCR): Checklist and description. Anne. Intern. Pharmaceuticals. 169, 467–473 (2018).
Covidence Systematic Review Software. Veritas Health Innovation.