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General purpose AI: Transitioning from high performance in highly curated settings to useful applications in ophthalmology

Artificial intelligence (AI) may be understood as the ability of machines to perform tasks which otherwise require human perception, reasoning, or learning. With the advent of deep learning, AI has achieved remarkable results across a wide range of medical tasks...

Ophthalmology assessment using Revised Bloom’s Taxonomy

Bloom’s Taxonomy is a hierarchical list of attributes and skills that facilitates teachers to effectively teach, whilst concurrently enabling learners to effectively learn. It was first developed in 1956 by Benjamin Bloom, an educational psychologist along with a team of...

Enhancing ophthalmology training: Unleashing the power of simulation

Ethical and practical barriers hinder early surgical experience in ophthalmology training [1], which is problematic when early-career trainees bring a higher risk of poor surgical outcomes and patient harm [2]. The European working time directive and the surgical backlog secondary...

Randomisation, confounding & observational methods in ophthalmic epidemiology

Randomisation and confounding Understanding the relationship between an exposure and an outcome of interest is the central challenge in ophthalmic epidemiology. The exposure may be aetiological, taking the form of a putative risk biological factor, or therapeutic, in the form...