Hitoshi Tabuchi, Justin Engelmann, Fumiatsu Maeda, Ryo Nishikawa, Toshihiko Nagasawa, Tomofusa Yamauchi, Mao Tanabe, Masahiro Akada, Keita Kihara, Yasuyuki Nakae, Yoshiaki Kiuchi, Miguel O Bernabeu
BACKGROUND: Artificial intelligence (AI) in medical imaging diagnostics has huge potential, but human judgement is still indispensable. We propose an AI-aided teaching method that leverages generative AI to train students on many images while preserving patient privacy. METHODS: A web-based course was designed using 600 synthetic ultra-widefield (UWF) retinal images to teach students to detect disease in these images. The images were generated by stable diffusion, a large generative foundation model, which we fine-tuned with 6285 real UWF images from six categories: five retinal diseases (age-related macular degeneration, glaucoma, diabetic retinopathy, retinal detachment and retinal vein occlusion) and normal...
March 14, 2024: British Journal of Ophthalmology