The orbit's complex, pyramidal 3D structure makes reconstruction a significant surgical challenge. Restoring anatomical accuracy and functional volume with hand-bent titanium plates is often a test of approximation. However, the last decade has seen a paradigm shift. The integration of computer-aided design (CAD) and computer-aided manufacturing (CAM), specifically 3D printing, is moving orbital surgery from an art of estimation to a science of precision.

Climate change is profoundly influencing global eye health by exacerbating the incidence and distribution of both infectious and non-infectious eye diseases. This occurs primarily through mechanisms such as rising temperatures, heightened air pollution, and more frequent extreme weather events. These environmental shifts not only alter disease patterns but also impose significant economic burdens, particularly in resource-constrained settings. As global temperatures continue to rise, projections indicate a sustained increase in disease burden through 2050, underscoring the urgency of addressing these impacts. This article explores the specific eye diseases affected, vulnerable populations, challenges in prevention and management, and effective interventions, drawing on epidemiological and public health evidence.

Glucagon-like peptide-1 receptor agonists (GLP-1 RAs) are injectable or oral medications primarily used for type 2 diabetes mellitus and, increasingly, for obesity management. They act by enhancing glucose-dependent insulin secretion, suppressing glucagon, and promoting satiety, leading to improved glycaemic control and weight loss. The use of GLP-1 RAs has increased dramatically in recent years. For example, semaglutide prescriptions in the US rose from 2 million monthly in 2021 to 5 million in 2023, with a notable shift toward use in both diabetic and nondiabetic obese populations. Their impact on ocular health, however, is more complex, with both protective and adverse associations reported.

Gender imbalance within surgical specialties has long been recognised. Despite steady increases in the number of women entering medical school, most surgical specialties remain male-dominated, particularly at consultant level. According to the Royal College of Surgeons of England (RCS) and its Women in Surgery (WinS) data dashboard, women account for only around 40% of surgical trainees and approximately 17% of consultant surgeons in the UK, depending on specialty. These figures have changed little over the past decade, and although initiatives such as WinS have improved visibility and mentorship, gender balance has advanced more gradually in surgery than across medicine overall.

With ophthalmology consistently among the most competitive specialties, it is common for applicants to take an FY3 year before re-applying. Rather than viewing this as a gap year, it can be seen as a valuable period to bridge between foundation training and specialty entry, allowing time to develop clinical competence, academic productivity, and insight into the specialty.

Septo-optic dysplasia (SOD), previously known as de Morsier syndrome, is a rare congenital disorder that is characterized by a triad of optic nerve hypoplasia, pituitary hormone dysfunction and midline brain abnormalities (typically agenesis or hypoplasia of septum pellucidum and/or corpus callosum).

Artificial intelligence (AI) is rapidly reshaping modern medicine, with ophthalmology standing at the forefront of this technological revolution. AI refers to the ability of machines to perform tasks traditionally requiring human intellect such as learning, problem solving and decision making. With the development of deep learning, advanced neural networks can learn complex patterns from large datasets without step-by-step programming. AI systems have reached impressive levels of accuracy in analysing and processing complex datasets and images. Ophthalmology is particularly well suited to benefit from AI, due to its reliance on high-resolution imaging for diagnosis and treatment planning. Fundus photographs, optical coherence tomography (OCT), and fluorescein angiography generate large amounts of visual data for pattern recognition by machine learning algorithms.