Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Improving Translational Accuracy

Improving Translational Accuracy

Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...

Improving Translational Accuracy

Improving Translational Accuracy

Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Cpeb4 regulates cardiomyocyte apoptosis in heart failure with association to Eif4a2 splicing modulation.

Scientific reports·2026

Same author

The N‑Glycoproteomic Landscape of the Lung in Monocrotaline-Induced Pulmonary Arterial Hypertension.

ACS omega·2026

Same author

ACE2 ameliorates DOX-induced cardiotoxicity by suppressing excessive autophagy via the AMPK/mTOR signaling pathway.

Biochemical pharmacology·2026

Same author

Analysis of the epidemiological features and factors associated with falls among the elderly in urban and rural areas of Chongqing, China: a cross-sectional study.

BMC public health·2026

Same author

Global, regional, and national trends in blindness and vision loss, 1990-2021: a secondary ecological trend analysis based on modelled population estimates.

Journal of global health·2026

Same author

SignMoD: Sign Language Video Generation via Mixture of Diffusion.

IEEE transactions on pattern analysis and machine intelligence·2026

Search research articles

Related Experiment Video

Updated: May 11, 2026

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

Advancing ophthalmology with large language models: Applications, challenges, and future directions.

Qi Zhang¹, Shaopan Wang², Xu Wang¹

¹School of Computer, University of South China, Hengyang, 421001, China.

Survey of Ophthalmology

|March 3, 2025

Summary

Large language models (LLMs) show promise in transforming ophthalmology by aiding diagnosis and research, but face challenges like data privacy and AI hallucinations. Further development is needed for safe clinical integration of these AI tools.

Keywords:

Artificial intelligence Clinical applications Data privacy Protection Large language models (LLMs)Ophthalmology

More Related Videos

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

Published on: April 14, 2023

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

Related Experiment Videos

Last Updated: May 11, 2026

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

Published on: April 14, 2023

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

Area of Science:

Ophthalmology
Artificial Intelligence
Medical Informatics

Background:

Artificial intelligence (AI) and large language models (LLMs) are rapidly advancing.
LLMs have the potential to revolutionize medical practices, enhancing efficiency and quality of care.
Ophthalmology is a key area where LLMs can offer significant benefits.

Purpose of the Study:

To summarize the current applications of LLMs in ophthalmology.
To highlight the challenges and limitations of using LLMs in clinical ophthalmology.
To provide insights for the future integration of LLMs in ophthalmic practice.

Main Methods:

Literature review of recent advancements in LLM applications in ophthalmology.
Analysis of LLM capabilities in diagnosis, treatment, medical records, and research.
Identification and discussion of key challenges including knowledge boundaries, AI hallucinations, and data privacy.

Main Results:

LLMs assist in ophthalmic disease diagnosis and treatment recommendations.
LLMs improve efficiency in medical record-writing and provide educational support.
LLMs support ophthalmic researchers in data processing and innovative studies.
Challenges such as knowledge limitations, AI hallucinations, and data privacy concerns are prevalent.

Conclusions:

LLMs offer substantial support for both clinicians and researchers in ophthalmology.
Addressing challenges like AI hallucinations and data privacy is crucial for clinical adoption.
Further research and development are necessary for the safe and effective use of LLMs in ophthalmology.