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. 161 trainee orthoptists took the course. They were evaluated with two tests: one consisting of UWF images and another of standard field (SF) images, which the students had not encountered in the course. Both tests contained 120 real patient images, 20 per category. The students took both tests once before and after training, with a cool-off period in between. Results On average, students completed the course in 53 min, significantly improving their diagnostic accuracy. For UWF images, student accuracy increased from 43.6% to 74.1% (p<0.0001 by paired t-test), nearly matching the previously published state-of-the-art AI model’s accuracy of 73.3%. For SF images, student accuracy rose from 42.7% to 68.7% (p<0.0001), surpassing the state-of-the-art AI model’s 40%. Conclusion Synthetic images can be used effectively in medical education. We also found that humans are more robust to novel situations than AI models, thus showcasing human judgement’s essential role in medical diagnosis. Data are available upon reasonable request. No data are available. In this study, the synthetic images created and adopted for training purposes can be shared for scientific purposes. However, the patient images used for image generation and those used for evaluation tests cannot be shared due to the divided legal opinions on the sharing of medical images within Japan.

中文翻译：

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利用人工智能提高人类表现：利用合成图像进行高效视网膜疾病检测训练

背景人工智能（AI）在医学影像诊断领域有着巨大的潜力，但人类的判断仍然不可或缺。我们提出了一种人工智能辅助教学方法，利用生成式人工智能来训练学生使用许多图像，同时保护患者隐私。方法 使用 600 个合成的超宽视野 (UWF) 视网膜图像设计了一个基于网络的课程，教学生在这些图像中检测疾病。这些图像是通过稳定扩散（一个大型生成基础模型）生成的，我们对来自六类的 6285 个真实 UWF 图像进行了微调：五种视网膜疾病（年龄相关性黄斑变性、青光眼、糖尿病性视网膜病变、视网膜脱离和视网膜静脉阻塞）和正常的。161 名见习矫形师参加了该课程。他们通过两项测试进行评估：一项由 UWF 图像组成，另一项由标准场 (SF) 图像组成，这是学生在课程中未遇到过的。两项测试均包含 120 张真实患者图像，每个类别 20 张。学生们在训练前和训练后都参加了两次测试，中间有一段冷静期。结果 学生平均在 53 分钟内完成课程，显着提高了他们的诊断准确性。对于 UWF 图像，学生准确率从 43.6% 提高到 74.1%（通过配对 t 检验，p<0.0001），几乎与之前发布的最先进的 AI 模型 73.3% 的准确率相匹配。对于 SF 图像，学生准确率从 42.7% 上升到 68.7% (p<0.0001)，超过了最先进的 AI 模型的 40%。结论合成图像可以有效地应用于医学教育。我们还发现，人类比人工智能模型对新情况更加稳健，从而展示了人类判断在医学诊断中的重要作用。数据可根据合理要求提供。无可用数据。在本研究中，为训练目的创建和采用的合成图像可以出于科学目的而共享。然而，由于日本国内对共享医学图像的法律意见存在分歧，用于图像生成的患者图像和用于评估测试的患者图像无法共享。