Search Articles

View query in Help articles search

Search Results (1 to 10 of 54 Results)

Download search results: CSV END BibTex RIS


Quantification of Metamorphopsia Using a Smartphone-Based Hyperacuity Test in Patients With Idiopathic Epiretinal Membranes: Prospective Observational Study

Quantification of Metamorphopsia Using a Smartphone-Based Hyperacuity Test in Patients With Idiopathic Epiretinal Membranes: Prospective Observational Study

This prospective observational study was conducted at the department of ophthalmology of the Hanusch Hospital, Vienna, Austria. The study included 30 eyes of 30 patients who met the inclusion criteria and were scheduled for membrane peeling with vitrectomy for i ERM. Eligibility criteria included the presence of i ERM, age above 18 years, written informed consent, best distance-corrected visual acuity (DCVA) ≤1.0 log MAR and metamorphopsia detected on the Amsler grid.

Daria Amon, Christoph Leisser, Andreas Schlatter, Manuel Ruiss, Caroline Pilwachs, Natascha Bayer, Josef Huemer, Oliver Findl

JMIR Perioper Med 2025;8:e60959

Evaluating the Effectiveness of Large Language Models in Providing Patient Education for Chinese Patients With Ocular Myasthenia Gravis: Mixed Methods Study

Evaluating the Effectiveness of Large Language Models in Providing Patient Education for Chinese Patients With Ocular Myasthenia Gravis: Mixed Methods Study

However, another study revealed that LLMs achieved only a 45% accuracy rate in identifying information for retinal disease patients, indicating significant gaps in their clinical application in ophthalmology [19]. To date, no studies have assessed LLM performance in educating Chinese patients about patients with OMG. Although several studies have explored the application of LLMs in consultation processes and diagnostic capabilities within Chinese ophthalmology subspecialties, research remains limited.

Bin Wei, Lili Yao, Xin Hu, Yuxiang Hu, Jie Rao, Yu Ji, Zhuoer Dong, Yichong Duan, Xiaorong Wu

J Med Internet Res 2025;27:e67883

Barriers and Determinants of Referral Adherence in AI-Enabled Diabetic Retinopathy Screening for Older Adults in Northern India During the COVID-19 Pandemic: Mixed Methods Pilot Study

Barriers and Determinants of Referral Adherence in AI-Enabled Diabetic Retinopathy Screening for Older Adults in Northern India During the COVID-19 Pandemic: Mixed Methods Pilot Study

The nurse at the ophthalmology clinic invited older adult participants to the CHC for in-depth interviews, while ASHA workers contacted older adult participants in the community to conduct interviews at homes. The second group of participants, HCPs, were purposely selected according to their roles in diabetes and DR diagnosis, referral, and treatment (Table S1 in Multimedia Appendix 1).

Anshul Chauhan, Anju Goyal, Ritika Masih, Gagandeep Kaur, Lakshay Kumar, ­ Neha, Harsh Rastogi, Sonam Kumar, Bidhi Lord Singh, Preeti Syal, Vishali Gupta, Luke Vale, Mona Duggal

JMIR Form Res 2025;9:e67047

The Utility of a Smartphone-Based Retinal Imaging Device as a Screening Tool in an Outpatient Clinic Setting: Protocol for an Observational Study

The Utility of a Smartphone-Based Retinal Imaging Device as a Screening Tool in an Outpatient Clinic Setting: Protocol for an Observational Study

Such an intervention would address the loss of follow-up to specialty ophthalmology clinics for complete ophthalmologic examinations in low socioeconomic status patients with eye disease. Early intervention may allow for the timely, evidence-based management of eye disease and prevent late-stage morbidities such as progressive loss of vision and permanent blindness.

Ajay Mittal, Victor Sanchez, Navjot Singh Azad, Yaroslav Zuyev, Rafael Robles, Mark Sherwood

JMIR Res Protoc 2025;14:e52650

EyeGPT for Patient Inquiries and Medical Education: Development and Validation of an Ophthalmology Large Language Model

EyeGPT for Patient Inquiries and Medical Education: Development and Validation of an Ophthalmology Large Language Model

In ophthalmology, LLMs show promise both for ophthalmic certification exams [5] and interpreting imaging reports across various linguistic environments [6,7]. However, there are several limitations to existing LLMs. First, there are challenges with addressing specialized ophthalmology knowledge for general LLMs. Previous research has demonstrated the suboptimal performance of Chat GPT in ophthalmology, with only 15.4% of the responses graded as completely accurate in vitreoretinal disease [8].

Xiaolan Chen, Ziwei Zhao, Weiyi Zhang, Pusheng Xu, Yue Wu, Mingpu Xu, Le Gao, Yinwen Li, Xianwen Shang, Danli Shi, Mingguang He

J Med Internet Res 2024;26:e60063

Performance of ChatGPT in Ophthalmic Registration and Clinical Diagnosis: Cross-Sectional Study

Performance of ChatGPT in Ophthalmic Registration and Clinical Diagnosis: Cross-Sectional Study

Artificial intelligence (AI) has significantly advanced in health care, particularly in many areas of ophthalmology [1,2]. Chat GPT (Open AI) [3] is a generative AI featuring a chatbot interface. Benefiting from its expansive knowledge base and complex parameterization, it enables users to input queries and receive responses that showcase advanced, humanlike logic. Since its launch in November 2022, Chat GPT has quickly amassed a substantial user base.

Shuai Ming, Xi Yao, Xiaohong Guo, Qingge Guo, Kunpeng Xie, Dandan Chen, Bo Lei

J Med Internet Res 2024;26:e60226

Impact of Ophthalmic Knowledge Assessment Program Scores and Surgical Volume on Subspecialty Fellowship Application in Ophthalmology Residency: Retrospective Cohort Study

Impact of Ophthalmic Knowledge Assessment Program Scores and Surgical Volume on Subspecialty Fellowship Application in Ophthalmology Residency: Retrospective Cohort Study

The number of ophthalmology residents who pursue further fellowship training has been increasing for more than a decade [1]. In 2005, around 64% of ophthalmology residents in the United States pursued subspecialty training, while 36% pursued comprehensive ophthalmology [2]. However, between 2012 and 2017, the percentage of ophthalmology residents in the United States pursuing subspecialty training increased to 70.3% [3].

Amanda Kay Hertel, Radwan S Ajlan

JMIR Med Educ 2024;10:e60940