| [1] |
Jiang X, Yu J, Ye J, Jia W, Xu W, et al. 2023. A deep learning-based method for pediatric congenital heart disease detection with seven standard views in echocardiography. |
| [2] |
Yang Z, Wang D, Zhou F, Song D, Zhang Y, et al. 2024. Understanding natural language: potential application of large language models to ophthalmology. |
| [3] |
Pur DR, Lee-Wing N, Bona MD. 2023. The use of augmented reality and virtual reality for visual field expansion and visual acuity improvement in low vision rehabilitation: a systematic review. |
| [4] |
Zuo G, Wang R, Wan C, Zhang Z, Zhang S, et al. 2024. Unveiling the evolution of virtual reality in medicine: a bibliometric analysis of research hotspots and trends over the past 12 years. |
| [5] |
Joseph S, Selvaraj J, Mani I, Kumaragurupari T, Shang X, et al. 2024. Diagnostic accuracy of artificial intelligence-based automated diabetic retinopathy screening in real-world settings: a systematic review and meta-analysis. |
| [6] |
Fazlollahi AM, Bakhaidar M, Alsayegh A, Yilmaz R, Winkler-Schwartz A, et al. 2022. Effect of artificial intelligence tutoring vs expert instruction on learning simulated surgical skills among medical students: a randomized clinical trial. |
| [7] |
Jin K, Ye J. 2022. Artificial intelligence and deep learning in ophthalmology: Current status and future perspectives. |
| [8] |
Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, et al. 2021. Updating guidance for reporting systematic reviews: development of the PRISMA 2020 statement. |
| [9] |
Wu D, Xiang Y, Wu X, Yu T, Huang X, et al. 2020. Artificial intelligence-tutoring problem-based learning in ophthalmology clerkship. |
| [10] |
Fang Z, Xu Z, He X, Han W. 2022. Artificial intelligence-based pathologic myopia identification system in the ophthalmology residency training program. |
| [11] |
Muntean GA, Groza A, Marginean A, Slavescu RR, Steiu MG, et al. 2023. Artificial intelligence for personalised ophthalmology residency training. |
| [12] |
Laupichler MC, Hadizadeh DR, Wintergerst MWM, von der Emde L, Paech D, et al. 2022. Effect of a flipped classroom course to foster medical students' AI literacy with a focus on medical imaging: a single group pre-and post-test study. |
| [13] |
Han R, Yu W, Chen H, Chen Y. 2022. Using artificial intelligence reading label system in diabetic retinopathy grading training of junior ophthalmology residents and medical students. |
| [14] |
Sonmez SC, Sevgi M, Antaki F, Huemer J, Keane PA. 2024. Generative artificial intelligence in ophthalmology: current innovations, future applications and challenges. |
| [15] |
Xu Q, Han J, Song X, Zhao Y, Wu L, et al. 2022. The effectiveness of artificial intelligence-based automated grading and training system in education of manual detection of diabetic retinopathy. |
| [16] |
Yu F, Silva Croso G, Kim TS, Song Z, Parker F, et al. 2019. Assessment of automated identification of phases in videos of cataract surgery using machine learning and deep learning techniques. |
| [17] |
Al Hajj H, Lamard M, Conze PH, Roychowdhury S, Hu X, et al. 2019. CATARACTS: challenge on automatic tool annotation for cataRACT surgery. |
| [18] |
Tabuchi H, Engelmann J, Maeda F, Nishikawa R, Nagasawa T, et al. 2024. Using artificial intelligence to improve human performance: efficient retinal disease detection training with synthetic images. |
| [19] |
Upadhyaya S, Agarwal A, Rengaraj V, Srinivasan K, Newman Casey PA, et al. 2022. Validation of a portable, non-mydriatic fundus camera compared to gold standard dilated fundus examination using slit lamp biomicroscopy for assessing the optic disc for glaucoma. |
| [20] |
Song S, He G, Huang D, Li X, Wu Z, et al. 2024. Efficacy of pars plana vitrectomy combined with internal limiting membrane peeling and gas tamponade for treating myopic foveoschisis: a meta-analysis. |
| [21] |
Gurnani B, Kaur K. 2024. Leveraging ChatGPT for ophthalmic education: a critical appraisal. |
| [22] |
Tsai AS, Chou HD, Ling XC, Al-Khaled T, Valikodath N, et al. 2022. Assessment and management of retinopathy of prematurity in the era of anti-vascular endothelial growth factor (VEGF). |
| [23] |
Al-Khaled T, Mikhail M, Jonas KE, Wu WC, Anzures R, et al. 2019. Training of residents and fellows in retinopathy of prematurity around the world: an international web-based survey. |
| [24] |
Raman R, Srinivasan S, Virmani S, Sivaprasad S, Rao C, et al. 2019. Fundus photograph-based deep learning algorithms in detecting diabetic retinopathy. |
| [25] |
Liu SJ, Feng XJ, Wang JS, Xiao ZQ, Cheng PS. 2021. Market analysis and countermeasures of nucleic acid drugs in China. |
| [26] |
Watson SL, Le DT. 2024. Corneal neuropathic pain: a review to inform clinical practice. |
| [27] |
Peng J, Xie X, Lu Z, Xu Y, Xie M, et al. 2024. Generative adversarial networks synthetic optical coherence tomography images as an education tool for image diagnosis of macular diseases: a randomized trial. |
| [28] |
Guo DL, Xu FB, Gong YJ, Xiang YF, Li Q, et al. 2022. Research progress and application status of artificial intelligence in fundus image analysis. |
| [29] |
Liu R, Li Q, Xu F, Wang S, He J, et al. 2022. Application of artificial intelligence-based dual-modality analysis combining fundus photography and optical coherence tomography in diabetic retinopathy screening in a community hospital. |
| [30] |
Succar T, Grigg J R. 2023. The science of virtual teaching and learning for ophthalmology medical education. In In their own words: what scholars and teachers want you to know about why and how to apply the science of learning in your academic setting, eds. Overson CE, Hakala CM, Kordonowy LL, Benassi VA. Society for the Teaching of Psychology. pp. 505−10 https://teachpsych.org/ebooks/itow |
| [31] |
Faes L, Fu DJ, Huemer J, Kern C, Wagner SK, et al. 2021. A virtual-clinic pathway for patients referred from a national diabetes eye screening programme reduces service demands whilst maintaining quality of care. |
| [32] |
Bakshi SK, Lin SR, Ting DSW, Chiang MF, Chodosh J. 2021. The era of artificial intelligence and virtual reality: transforming surgical education in ophthalmology. |
| [33] |
Chia MA, Turner AW. 2022. Benefits of integrating telemedicine and artificial intelligence into outreach eye care: stepwise approach and future directions. |
| [34] |
Long E, Chen J, Wu X, Liu Z, Wang L, et al. 2020. Artificial intelligence manages congenital cataract with individualized prediction and telehealth computing. |
| [35] |
Ferro Desideri L, Rutigliani C, Corazza P, Nastasi A, Roda M, et al. 2022. The upcoming role of Artificial Intelligence (AI) for retinal and glaucomatous diseases. |
| [36] |
Abràmoff MD, Lavin PT, Birch M, Shah N, Folk JC. 2018. Pivotal trial of an autonomous AI-based diagnostic system for detection of diabetic retinopathy in primary care offices. |
| [37] |
Thakoor KA, Yao J, Bordbar D, Moussa O, Lin W, et al. 2022. A multimodal deep learning system to distinguish late stages of AMD and to compare expert vs. AI ocular biomarkers. |
| [38] |
Al-Aswad LA, Kapoor R, Chu CK, Walters S, Gong D, et al. 2019. Evaluation of a deep learning system for identifying glaucomatous optic neuropathy based on color fundus photographs. |
| [39] |
von der Emde L, Künzel SH, Pfau M, Morelle O, Liermann Y, et al. 2024. Use of artificial intelligence for recognition of biomarkers in intermediate age-related macular degeneration. |
| [40] |
Chang P, von der Emde L, Pfau M, Künzel S, Fleckenstein M, et al. 2024. Use of artificial intelligence in geographic atrophy in age-related macular degeneration. |
| [41] |
Succar T, Grigg J, Beaver HA, Lee AG. 2020. Advancing ophthalmology medical student education: international insights and strategies for enhanced teaching. |
| [42] |
Valikodath NG, Cole E, Ting DSW, Campbell JP, Pasquale LR, et al. 2021. Impact of artificial intelligence on medical education in ophthalmology. |
| [43] |
Peng Z, Wu CR, Zhang X. 2021. Application status and research progress of virtual reality technology in ophthalmology. |
| [44] |
Dyer E, Swartzlander BJ, Gugliucci MR. 2018. Using virtual reality in medical education to teach empathy. |
| [45] |
Carr L, McKechnie T, Hatamnejad A, Chan J, Beattie A. 2024. Effectiveness of the Eyesi Surgical Simulator for ophthalmology trainees: systematic review and meta-analysis. |
| [46] |
Joo HJ, Jeong HY. 2020. A study on eye-tracking-based interface for VR/AR education platform. |
| [47] |
Tu P, Ye H, Shi H, Young J, Xie M, et al. 2025. Phase-specific augmented reality guidance for microscopic cataract surgery using spatiotemporal fusion network. |
| [48] |
Alwadani F, Morsi MS. 2012. PixEye virtual reality training has the potential of enhancing proficiency of laser trabeculoplasty performed by medical students: a pilot study. |
| [49] |
Reipschläger P, Dachselt R. 2019. DesignAR: immersive 3D-modeling combining augmented reality with interactive displays. Proceedings of the 2019 ACM International Conference on Interactive Surfaces and Spaces, Daejeon Republic of Korea, 10−13, November, 2019. New York, USA: Association for Computing Machinery (ACM). pp. 29−41 doi: 10.1145/3343055.3359718 |
| [50] |
Liu XF, Sun XY, Zhu X. 2021. Current situation and challenges facing artificial intelligence in its application in new drug research and development. Progress in Pharmaceutical Sciences 45(7):494−501 (in Chinese) |
| [51] |
Dong L, He W, Zhang R, Ge Z, Wang YX, et al. 2022. Artificial intelligence for screening of multiple retinal and optic nerve diseases. |
| [52] |
Wei WB, Dong L, Zhang RH, Wang HY. 2024. Preliminary exploration of the application of artificial intelligence generative adversarial networks in ophthalmology clinical teaching. |
| [53] |
Krause J, Gulshan V, Rahimy E, Karth P, Widner K, et al. 2018. Grader variability and the importance of reference standards for evaluating machine learning models for diabetic retinopathy. |
| [54] |
Hood DC, La Bruna S, Tsamis E, Thakoor KA, Rai A, et al. 2022. Detecting glaucoma with only OCT: implications for the clinic, research, screening, and AI development. |
| [55] |
Gunasekeran DV, Ting DSW, Tan GSW, Wong TY. 2020. Artificial intelligence for diabetic retinopathy screening, prediction and management. |
| [56] |
Wang XH, Bryan S, Cheng W. 2021. The application of artificial intelligence in ophthalmic medical management: challenges and prospects. |
| [57] |
Wang YX, Xue CC, Li JJ. 2021. Main problems and strategies in ophthalmic artificial intelligence research. |
| [58] |
Li Z, Guo C, Lin D, Nie D, Zhu Y, et al. 2021. Deep learning for automated glaucomatous optic neuropathy detection from ultra-widefield fundus images. |
| [59] |
Alhaidry HM, Fatani B, Alrayes JO, Almana AM, Alfhaed NK. 2023. ChatGPT in dentistry: a comprehensive review. |
| [60] |
Liu X, Faes L, Kale AU, Wagner SK, Fu DJ, et al. 2019. A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: a systematic review and meta-analysis. |
| [61] |
Abdullah YI, Schuman JS, Shabsigh R, Caplan A, Al-Aswad LA. 2021. Ethics of artificial intelligence in medicine and ophthalmology. |
| [62] |
Zisimopoulos O, Flouty E, Luengo I, Giataganas P, Nehme J, et al. 2018. DeepPhase: surgical phase recognition in CATARACTS videos. In Medical Image Computing and Computer Assisted Intervention – MICCAI 2018, eds. Frangi A, Schnabel J, Davatzikos C, Alberola-López C, Fichtinger G. Cham: Springer, pp. 265−72 doi: 10.1007/978-3-030-00937-3_31 |