ReviewCurrent opinion in ophthalmology2024
Applications of artificial intelligence-enabled robots and chatbots in ophthalmology: recent advances and future trends.
Review in Current opinion in ophthalmology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
13 citing papers in PubMed.
- Comparative evaluation of large language models and clinicians in real-world glaucoma clinical reasoning.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2026Article
- JustRAIGS: Justified Referral in AI Glaucoma Screening Challenge.IEEE transactions on medical imaging · 2026Article
- GlaucoRAG: A Retrieval-Augmented Large Language Model for Expert-Level Glaucoma Assessment.medRxiv : the preprint server for health sciences · 2025Article
- Large Language Models: Pioneering New Educational Frontiers in Childhood Myopia.Ophthalmology and therapy · 2025Article
- Navigating the AI Revolution in Medicine-Adopting Strategies for Medical Education.Medical science educator · 2025Article
- Article
- Assessment of Large Language Models in Cataract Care Information Provision: A Quantitative Comparison.Ophthalmology and therapy · 2025Article
- Performance of ChatGPT in Ophthalmic Registration and Clinical Diagnosis: Cross-Sectional Study.Journal of medical Internet research · 2024Article
- Assessing large language models' accuracy in providing patient support for choroidal melanoma.Eye (London, England) · 2024Article
- ChatGPT Assisting Diagnosis of Neuro-Ophthalmology Diseases Based on Case Reports.Journal of neuro-ophthalmology : the official journal of the North American Neuro-Ophthalmology Society · 2024Article
- Exploring the Potential of Code-Free Custom GPTs in Ophthalmology: An Early Analysis of GPT Store and User-Creator Guidance.Ophthalmology and therapy · 2024Article
- Retinal Vein Occlusion-Background Knowledge and Foreground Knowledge Prospects-A Review.Journal of clinical medicine · 2024Review
- Large Language Models in Ophthalmology: A Review of Publications from Top Ophthalmology Journals.Ophthalmology scienceArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
purpose of reviewRecent advances in artificial intelligence (AI), robotics, and chatbots have brought these technologies to the forefront of medicine, particularly ophthalmology. These technologies have been applied in diagnosis, prognosis, surgical operations, and patient-specific care in ophthalmology. It is thus both timely and pertinent to assess the existing landscape, recent advances, and trajectory of trends of AI, AI-enabled robots, and chatbots in ophthalmology. RECENT
findingsSome recent developments have integrated AI enabled robotics with diagnosis, and surgical procedures in ophthalmology. More recently, large language models (LLMs) like ChatGPT have shown promise in augmenting research capabilities and diagnosing ophthalmic diseases. These developments may portend a new era of doctor-patient-machine collaboration. SUMMARY: Ophthalmology is undergoing a revolutionary change in research, clinical practice, and surgical interventions. Ophthalmic AI-enabled robotics and chatbot technologies based on LLMs are converging to create a new era of digital ophthalmology. Collectively, these developments portend a future in which conventional ophthalmic knowledge will be seamlessly integrated with AI to improve the patient experience and enhance therapeutic outcomes.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.