AIMC Topic: Ophthalmologists
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Real-world artificial intelligence-based interpretation of fundus imaging as part of an eyewear prescription renewal protocol.
Journal francais d'ophtalmologie
Mar 8, 2024
OBJECTIVE: A real-world evaluation of the diagnostic accuracy of the Opthai® software for artificial intelligence-based detection of fundus image abnormalities in the context of the French eyewear prescription renewal protocol (RNO).
Evaluating the accuracy of the Ophthalmologist Robot for multiple blindness-causing eye diseases: a multicentre, prospective study protocol.
BMJ open
Mar 1, 2024
INTRODUCTION: Early eye screening and treatment can reduce the incidence of blindness by detecting and addressing eye diseases at an early stage. The Ophthalmologist Robot is an automated device that can simultaneously capture ocular surface and fund...
Ensemble of deep convolutional neural networks is more accurate and reliable than board-certified ophthalmologists at detecting multiple diseases in retinal fundus photographs.
The British journal of ophthalmology
Feb 21, 2024
AIMS: To develop an algorithm to classify multiple retinal pathologies accurately and reliably from fundus photographs and to validate its performance against human experts.
Leveraging ChatGPT for ophthalmic education: A critical appraisal.
European journal of ophthalmology
Nov 16, 2023
In recent years, the advent of artificial intelligence (AI) has transformed many sectors, including medical education. This editorial critically appraises the integration of ChatGPT, a state-of-the-art AI language model, into ophthalmic education, fo...
Clinical evaluation of deep learning systems for assisting in the diagnosis of the epiretinal membrane grade in general ophthalmologists.
Eye (London, England)
Oct 17, 2023
BACKGROUND: Epiretinal membrane (ERM) is a common age-related retinal disease detected by optical coherence tomography (OCT), with a prevalence of 34.1% among people over 60 years old. This study aims to develop artificial intelligence (AI) systems t...
Chatbots Vs. Human Experts: Evaluating Diagnostic Performance of Chatbots in Uveitis and the Perspectives on AI Adoption in Ophthalmology.
Ocular immunology and inflammation
Oct 13, 2023
PURPOSE: To assess the diagnostic performance of two chatbots, ChatGPT and Glass, in uveitis diagnosis compared to renowned uveitis specialists, and evaluate clinicians' perception about utilizing artificial intelligence (AI) in ophthalmology practic...
Evaluating the Diagnostic Accuracy and Management Recommendations of ChatGPT in Uveitis.
Ocular immunology and inflammation
Sep 18, 2023
INTRODUCTION: Accurate diagnosis and timely management are vital for favorable uveitis outcomes. Artificial Intelligence (AI) holds promise in medical decision-making, particularly in ophthalmology. Yet, the diagnostic precision and management advice...
Artificial intelligence chatbot performance in triage of ophthalmic conditions.
Canadian journal of ophthalmology. Journal canadien d'ophtalmologie
Aug 9, 2023
BACKGROUND: Timely access to human expertise for affordable and efficient triage of ophthalmic conditions is inconsistent. With recent advancements in publicly available artificial intelligence (AI) chatbots, the lay public may turn to these tools fo...
Comparison of Ophthalmologist and Large Language Model Chatbot Responses to Online Patient Eye Care Questions.
JAMA network open
Aug 1, 2023
IMPORTANCE: Large language models (LLMs) like ChatGPT appear capable of performing a variety of tasks, including answering patient eye care questions, but have not yet been evaluated in direct comparison with ophthalmologists. It remains unclear whet...
Automated quantification of meibomian gland dropout in infrared meibography using deep learning.
The ocular surface
Jun 24, 2022
PURPOSE: Develop a deep learning-based automated method to segment meibomian glands (MG) and eyelids, quantitatively analyze the MG area and MG ratio, estimate the meiboscore, and remove specular reflections from infrared images.
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