Discover the newest research about AI innovations in ποΈ Ophthalmology.
Deep learning model detects orbital fractures in youth radiographs: AUROC 0.802, sensitivity 65.8%, specificity 86.5%. πποΈ
Retinopathy of Prematurity: Anti-VEGF Impact & AI’s Role in Screening ππΆ
NLP identifies transportation insecurity in 0.6% of ophthalmology patients, enhancing access to care. πποΈ
AI enhances Optical Coherence Tomography (OCT) for disease detection, improving diagnostic accuracy and patient outcomes. ππ©Ί
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
