A5093941106- Retinal Imaging and Analysis
- Artificial Intelligence in Healthcare and Education
- Corneal surgery and disorders
- Corneal Surgery and Treatments
- Glaucoma and retinal disorders
- COVID-19 diagnosis using AI
- Retinal and Optic Conditions
- Ocular Surface and Contact Lens
- Digital Imaging for Blood Diseases
- Retinal and Macular Surgery
- Ophthalmology and Visual Health Research
- Health Systems, Economic Evaluations, Quality of Life
- Clinical Reasoning and Diagnostic Skills
- Machine Learning in Healthcare
- Ocular Infections and Treatments
University of Toronto
2024-2025
Université de Montréal
2024
This study assesses the proficiency of Generative Pre-trained Transformer (GPT)-4 in answering questions about complex clinical ophthalmology cases.
Purpose: To compare the performance and cost-effectiveness of DeepSeek-R1 with OpenAI o1 in diagnosing managing ophthalmology clinical cases. Study Design: Cross-sectional evaluation. Methods: A total 300 cases spanning 10 different subspecialties were collected from StatPearls. Each case presented a multiple-choice question regarding diagnosis or management case. was accessed through its public chat-based interface, while queried via an Application Program Interface (API) standardized...
We read with great interest the article “Clinical Outcomes and Early Postoperative Complications in Boston Type I Keratoprosthesis Implantation: A Retrospective Study” by Krysik et al [...]
To assess the performance of Chat Generative Pre-Trained Transformer-4 in providing accurate diagnoses to retina teaching cases from OCTCases.
To evaluate the performance of large language models (LLMs), specifically Microsoft Copilot, GPT-4 (GPT-4o and GPT-4o mini), Google Gemini (Gemini Advanced), in answering ophthalmological questions assessing impact prompting techniques on their accuracy. Prospective qualitative study. Advanced). A total 300 from StatPearls were tested, covering a range subspecialties image-based tasks. Each question was evaluated using 2 techniques: zero-shot forced (prompt 1) combined role-based...
Mikhail, David MD(C), MSc(C); Milad, Daniel MD; Harissi-Dagher, Mona MD, FRCSC Author Information
Objectives: The integration of multimodal capabilities into GPT-4 represents a transformative leap for artificial intelligence in ophthalmology, yet its utility scenarios requiring advanced reasoning remains underexplored. This study evaluates GPT-4’s performance on open-ended diagnostic and next-step tasks complex ophthalmology cases, comparing it against human expertise. Methods: was assessed across three arms: (1) text-based case details with figure descriptions, (2) cases text...