Artificial Intelligence Applications in Ophthalmology

Dear Colleagues,

Artificial intelligence (AI) applications in ophthalmology have emerged as transformative tools, reshaping various facets of eye care. Leveraging AI's capacity to analyze extensive datasets and interpret the intricate imaging, automated diagnosis and early detection of ocular diseases, including diabetic retinopathy and glaucoma, has become more precise. Machine learning algorithms exhibit exceptional accuracy in image analysis, facilitating swift and accurate diagnoses by clinicians. Furthermore, predictive modeling enhances prognosis and contributes to personalized treatment plans. The integration of AI not only streamlines clinical workflows and enhances efficiency, but also holds the promise in broadening access to eye care, especially in remote or underserved regions. Despite these promising advancements, numerous challenges and ethical concerns must be addressed before the safe deployment of AI in ophthalmic care.

Ensuring the validation of the quality and fairness of AI-generated outputs, safeguarding patient privacy and optimizing collaboration between AI and healthcare professionals are imperative for achieving optimal patient outcomes wherever AI tools are employed.

This Special Issue is dedicated to exploring advancements in the realm of AI applications within ophthalmology. We invite contributions that illustrate the diverse ways in which AI can positively impact various facets of eye care. Potential topics of interest include, but are not limited to:

• Anomaly detection in ophthalmic imaging;
• Bias/fairness of AI models in ophthalmology;
• Applications of diffusion /generative models in ophthalmology;
• Domain adaptation, domain generalization, data harmonization and transfer learning techniques;
• Federated learning techniques in ophthalmology;
• Few-shot learning techniques in ophthalmology;
• Interpretability of AI models in ophthalmology;
• Multi-modality AI models in ophthalmology;
• Applications of multi-task learning in ophthalmology;
• Out-of-distribution detection techniques in ophthalmology;
• Self-supervised learning applications in ophthalmology;
• Weakly supervised learning / multi-instance learning applications in ophthalmology
• Image registration of ophthalmic imaging;
• Uncertainty quantification of AI models in ophthalmology;
• Applications of medical phrase grounding/visual question answering/ChatGPT in ophthalmology.

Dr. Praveer Singh
Dr. Liangqiong Qu
Guest Editors

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• artificial intelligence (AI)
• deep learning
• medical imaging
• ophthalmology
• ocular diseases