| Application of Comprehensive Artificial intelligence Retinal Expert (CARE) system: a national real-world evidence study D Lin, J Xiong, C Liu, L Zhao, Z Li, S Yu, X Wu, Z Ge, X Hu, B Wang, M Fu, ... The Lancet Digital Health 3 (8), e486-e495, 2021 | 114 | 2021 |
| Preventing corneal blindness caused by keratitis using artificial intelligence Z Li, J Jiang, K Chen, Q Chen, Q Zheng, X Liu, H Weng, S Wu, W Chen Nature communications 12 (1), 3738, 2021 | 93 | 2021 |
| Deep learning for detecting retinal detachment and discerning macular status using ultra-widefield fundus images Z Li, C Guo, D Nie, D Lin, Y Zhu, C Chen, X Wu, F Xu, C Jin, X Zhang, ... Communications biology 3 (1), 15, 2020 | 82 | 2020 |
| Artificial intelligence in ophthalmology: The path to the real-world clinic Z Li, L Wang, X Wu, J Jiang, W Qiang, H Xie, H Zhou, S Wu, Y Shao, ... Cell Reports Medicine, 2023 | 68 | 2023 |
| A deep learning system for identifying lattice degeneration and retinal breaks using ultra-widefield fundus images Z Li, C Guo, D Nie, D Lin, Y Zhu, C Chen, L Zhang, F Xu, C Jin, X Zhang, ... Annals of translational medicine 7 (22), 2019 | 50 | 2019 |
| Deep learning for automated glaucomatous optic neuropathy detection from ultra-widefield fundus images Z Li, C Guo, D Lin, D Nie, Y Zhu, C Chen, L Zhao, J Wang, X Zhang, ... British journal of ophthalmology 105 (11), 1548-1554, 2021 | 49 | 2021 |
| Artificial intelligence to detect malignant eyelid tumors from photographic images Z Li, W Qiang, H Chen, M Pei, X Yu, L Wang, Z Li, W Xie, X Wu, J Jiang, ... NPJ digital medicine 5 (1), 23, 2022 | 32 | 2022 |
| Automated detection of retinal exudates and drusen in ultra-widefield fundus images based on deep learning Z Li, C Guo, D Nie, D Lin, T Cui, Y Zhu, C Chen, L Zhao, X Zhang, ... Eye 36 (8), 1681-1686, 2022 | 30 | 2022 |
| Development and evaluation of a deep learning system for screening retinal hemorrhage based on ultra-widefield fundus images Z Li, C Guo, D Nie, D Lin, Y Zhu, C Chen, Y Xiang, F Xu, C Jin, X Zhang, ... Translational vision science & technology 9 (2), 3-3, 2020 | 30 | 2020 |
| Deep learning from “passive feeding” to “selective eating” of real-world data Z Li, C Guo, D Nie, D Lin, Y Zhu, C Chen, L Zhao, X Wu, M Dongye, F Xu, ... NPJ digital medicine 3 (1), 143, 2020 | 23 | 2020 |
| Development of a deep learning-based image quality control system to detect and filter out ineligible slit-lamp images: A multicenter study Z Li, J Jiang, K Chen, Q Zheng, X Liu, H Weng, S Wu, W Chen Computer Methods and Programs in Biomedicine 203, 106048, 2021 | 13 | 2021 |
| Development of a deep learning-based image eligibility verification system for detecting and filtering out ineligible fundus images: a multicentre study Z Li, J Jiang, H Zhou, Q Zheng, X Liu, K Chen, H Weng, W Chen International journal of medical informatics 147, 104363, 2021 | 11 | 2021 |
| Comparison of deep learning systems and cornea specialists in detecting corneal diseases from low-quality images Z Li, J Jiang, W Qiang, L Guo, X Liu, H Weng, S Wu, Q Zheng, W Chen Iscience 24 (11), 2021 | 10 | 2021 |
| Solving data quality issues of fundus images in real-world settings by ophthalmic AI Z Li, W Chen Cell Reports Medicine 4 (2), 2023 | 2 | 2023 |