Published on in Vol 3 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/58342, first published .
Feasibility of Multimodal Artificial Intelligence Using GPT-4 Vision for the Classification of Middle Ear Disease: Qualitative Study and Validation

Feasibility of Multimodal Artificial Intelligence Using GPT-4 Vision for the Classification of Middle Ear Disease: Qualitative Study and Validation

Feasibility of Multimodal Artificial Intelligence Using GPT-4 Vision for the Classification of Middle Ear Disease: Qualitative Study and Validation

Journals

  1. Terwilliger E, Bcharah G, Bcharah H, Bcharah E, Richardson C, Scheffler P. Advancing Medical Education: Performance of Generative Artificial Intelligence Models on Otolaryngology Board Preparation Questions With Image Analysis Insights. Cureus 2024 View
  2. Nomura A, Takeji Y, Shimojima M, Takamura M. Digitalomics: Towards Artificial Intelligence / Machine Learning-Based Precision Cardiovascular Medicine. Circulation Journal 2025 View
  3. Banyi N, Ma B, Amanian A, Bur A, Abdalkhani A. Applications of Natural Language Processing in Otolaryngology: A Scoping Review. The Laryngoscope 2025;135(9):3049 View
  4. Filali Ansary R, Lechien J. Clinical decision support using large language models in otolaryngology: a systematic review. European Archives of Oto-Rhino-Laryngology 2025;282(8):4325 View
  5. Zhou S, Xu Z, Zhang M, Xu C, Guo Y, Zhan Z, Fang Y, Ding S, Wang J, Xu K, Xia L, Yeung J, Zha D, Cai D, Melton G, Lin M, Zhang R. Large language models for disease diagnosis: a scoping review. npj Artificial Intelligence 2025;1(1) View

Books/Policy Documents

  1. Sultana J, Qin R, Yin Z. Computer Vision – ACCV 2024. View

Conference Proceedings

  1. Deva B, Sood V, Fahim A, Krishna P, T A. 2025 3rd International Conference on Data Science and Information System (ICDSIS). Real Time Diagnosis of Middle Ear Infections using Raspberry Pi integrated Vision Transformer System with Feature Fusion View