AI Medical Compendium Journal:
Eye (London, England)

Showing 1 to 10 of 67 articles

Comparative performance analysis of global and chinese-domain large language models for myopia.

Eye (London, England)
BACKGROUND: The performance of global large language models (LLMs), trained largely on Western data, for disease in other settings and languages is unknown. Taking myopia as an illustration, we evaluated the global versus Chinese-domain LLMs in addre...

Chat GPT vs an experienced ophthalmologist: evaluating chatbot writing performance in ophthalmology.

Eye (London, England)
PURPOSE: To examine the abilities of ChatGPT in writing scientific ophthalmology introductions and to compare those abilities to experienced ophthalmologists.

AI for glaucoma, Are we reporting well? a systematic literature review of DECIDE-AI checklist adherence.

Eye (London, England)
BACKGROUND/OBJECTIVES: This systematic literature review examines the quality of early clinical evaluation of artificial intelligence (AI) decision support systems (DSS) reported in glaucoma care. Artificial Intelligence applications within glaucoma ...

Deep learning model for automatic detection of different types of microaneurysms in diabetic retinopathy.

Eye (London, England)
PURPOSE: This study aims to develop a deep-learning-based software capable of detecting and differentiating microaneurysms (MAs) as hyporeflective or hyperreflective on structural optical coherence tomography (OCT) images in patients with non-prolife...

Neural networks for predicting etiological diagnosis of uveitis.

Eye (London, England)
BACKGROUND/OBJECTIVES: The large number and heterogeneity of causes of uveitis make the etiological diagnosis a complex task. The clinician must consider all the information concerning the ophthalmological and extra-ophthalmological features of the p...

Benchmarking the performance of large language models in uveitis: a comparative analysis of ChatGPT-3.5, ChatGPT-4.0, Google Gemini, and Anthropic Claude3.

Eye (London, England)
BACKGROUND/OBJECTIVE: This study aimed to evaluate the accuracy, comprehensiveness, and readability of responses generated by various Large Language Models (LLMs) (ChatGPT-3.5, Gemini, Claude 3, and GPT-4.0) in the clinical context of uveitis, utiliz...

The use of artificial intelligence based chat bots in ophthalmology triage.

Eye (London, England)
PURPOSE: To evaluate AI-based chat bots ability to accurately answer common patient's questions in the field of ophthalmology.

Artificial intelligence in assessing progression of age-related macular degeneration.

Eye (London, England)
The human population is steadily growing with increased life expectancy, impacting the prevalence of age-dependent diseases, including age-related macular degeneration (AMD). Health care systems are confronted with an increasing burden with rising pa...

A generalised computer vision model for improved glaucoma screening using fundus images.

Eye (London, England)
IMPORTANCE: Worldwide, glaucoma is a leading cause of irreversible blindness. Timely detection is paramount yet challenging, particularly in resource-limited settings. A novel, computer vision-based model for glaucoma screening using fundus images co...