The Wilmer Institute Mentored Clinician Scientist Scholar Program-Supplement

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PROJECT SUMMARY/ ABSTRACT Artificial intelligence (AI), in the form of machine learning (ML) and deep learning (DL), has revolutionized the field of medicine, especially in subspecialties with access to a large number of images, such as ophthalmology. While promising, the application of ML/AI in ophthalmology is limited by the lack of clear guidelines on how to extract ophthalmic data, convert these data into a form that is usable for ML/AI model training and ensure these data are adhering to the principles of FAIR (Findability, Accessibility, Interoperability, and Reuse). Our proposal aims to address these limitations by delineating the best practices for data curation via an online lecture series that will be available free of charge to the public, once the curriculum is assessed and validated by another academic institution (Harvard/Massachusetts Eye and Ear Infirmary). If successful, the proposal will be impactful, as it will: 1. Accelerate reproducible research in ML/AI by enabling the creation of standardized ML/AI-ready and FAIR datasets. 2. Disseminate the best practices in data curation beyond Johns Hopkins University (JHU), as the online lectures will be made free of charge to the public. 3. Foster the creation of standardized multi-institutional datasets that will improve both performance and generalizability of ML/AI algorithms in ophthalmology, by enabling federated learning approaches and external validation of models.