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. 2023 Nov 29;25:e48142. doi: 10.2196/48142

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©Jeewoo Yoon, Jinyoung Han, Junseo Ko, Seong Choi, Ji In Park, Joon Seo Hwang, Jeong Mo Han, Daniel Duck-Jin Hwang. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 29.11.2023.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included.

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An illustration of the experimental procedure. In total, 66 ophthalmologists participated in the experiment. All observers were instructed to select one of the possible retinal diseases after reviewing the given SD-OCT image. In step 1, only the SD-OCT image was provided without any AI advice (ie, No AI). In step 2, the decision probability provided by AI was displayed on the system (ie, AI Prob). Finally, in step 3, the visual evidence of the AI decision was provided in addition to the AI probability panel (ie, AI Prob+Evid). The AI model was trained, validated, and tested using 1389, 141, and 163 images, respectively. The experiment was conducted using 100 images randomly sampled from the test set. AI: artificial intelligence; AI Prob+Evid: artificial intelligence assistance with a probability score and visual evidence heatmap; AI Prob: artificial intelligence assistance with a probability score; No AI: without artificial intelligence assistance; SD-OCT: spectral domain optical coherence tomography.