Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
Editorial
. 2023 Aug 30;11(10):337.
doi: 10.21037/atm-23-1598. Epub 2023 Jun 30.

New era after ChatGPT in ophthalmology: advances from data-based decision support to patient-centered generative artificial intelligence

Joon Yul Choi  1 , Tae Keun Yoo  2   3
Affiliations
Editorial

New era after ChatGPT in ophthalmology: advances from data-based decision support to patient-centered generative artificial intelligence

Joon Yul Choi et al. Ann Transl Med. .
No abstract available

Keywords: AI; ChatGPT; generative artificial intelligence (generative AI); ophthalmology.

PubMed Disclaimer

Conflict of interest statement

Conflicts of Interest: Both authors have completed the ICMJE uniform disclosure form (available at https://atm.amegroups.com/article/view/10.21037/atm-23-1598/coif). TKY reported that he served as a speaker for an academic lecture in VUNO and Hangil Eye Hospital, and served as a lecturer for a commercial conference held by the Korea Association of Intelligence Wellcare Industries (KIWI). TKY is an employee of B&VIIT Eye Center and VISUWORKS. He received a salary as part of the standard compensation package. He also received research grants for refractive surgery from Carl Zeiss Meditec AG. The research grants did not affect this manuscript. The other author has no conflicts of interest to declare.

Figures

Figure 1
Figure 1
History of development of generative AI. AI, artificial intelligence; VAE, variational autoencoder; GAN, generative adversarial network; DCGAN, deep convolutional generative adversarial network; VQ, vector quantized; ViT, vision transformer; DDPM, denoising diffusion probabilistic model; DDIM, denoising diffusion implicit model; GLIDE, guided language-to-image diffusion for generation and editing; GRU, gated recurrent unit; GPT, generative pre-trained transformer; BERT, bidirectional encoder representations from transformers; LaMDA, Language Model for Dialogue Applications; LLaMA; Large Language Model Meta AI.
See this image and copyright information in PMC

Comment on

References

    1. Jiang X, Xie M, Ma L, et al. International publication trends in the application of artificial intelligence in ophthalmology research: an updated bibliometric analysis. Ann Transl Med 2023;11:219. 10.21037/atm-22-3773 - DOI - PMC - PubMed
    1. Jin K, Ye J. Artificial intelligence and deep learning in ophthalmology: current status and future perspectives. Adv Ophthalmol Pract Res 2022;2:100078. 10.1016/j.aopr.2022.100078 - DOI - PMC - PubMed
    1. Gulshan V, Peng L, Coram M, et al. Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs. JAMA 2016;316:2402-10. 10.1001/jama.2016.17216 - DOI - PubMed
    1. Keenan TDL, Chen Q, Agrón E, et al. DeepLensNet: Deep Learning Automated Diagnosis and Quantitative Classification of Cataract Type and Severity. Ophthalmology 2022;129:571-84. 10.1016/j.ophtha.2021.12.017 - DOI - PMC - PubMed
    1. Abràmoff MD, Lavin PT, Birch M, et al. Pivotal trial of an autonomous AI-based diagnostic system for detection of diabetic retinopathy in primary care offices. NPJ Digit Med 2018;1:39. 10.1038/s41746-018-0040-6 - DOI - PMC - PubMed