
A Practical Guide to Evaluating Artificial Intelligence Imaging Models in Scientific Literature.
Evaluating AI in Ophthalmology: Key Insights from Recent Research π©Ίπ
Discover the newest research about AI innovations in ποΈ Ophthalmology.
Evaluating AI in Ophthalmology: Key Insights from Recent Research π©Ίπ
AI reveals sex-specific diabetic retinopathy patterns in retinal images. AUC scores: 0.72 & 0.75. Key findings on risk factors! ποΈπ
Call for standardisation of electronic health records in eye care aims to improve patient safety and care continuity. π₯ποΈ
“Exploring ROP Training Gaps: 70% of Ophthalmologists Lack Adequate Exposure πποΈ”
Lissamine Green: Key Diagnostic Tool in Ocular Surface Disease π₯ποΈ
Exploring Large Language Models in Healthcare: Transformative Potential & Ethical Considerations π€π
AI in anterior segment disease diagnosis shows 90% accuracy, enhancing personalized care and treatment outcomes. π€ποΈ
Federal grant of $2.9M supports digital dementia care for CALD communities, enhancing skills for informal carers. π§ π»
AI-SaMD: Key Findings & Challenges in Medical Applications π€π
Large language models show promise in educating Chinese patients with ocular myasthenia gravis. ππ€ Accuracy and readability are key factors.