Entropion is an inward turning of the eyelid margin, most often affecting the lower lid. It is one of the more common eyelid malpositions encountered in ophthalmology and oculoplastics clinics, yet it is easy for junior doctors to overlook, since patients often present with vague symptoms such as a gritty or watery eye rather than an obviously abnormal-looking lid. Recognising entropion matters because, left untreated, it can progress to genuine corneal damage.

Solar retinopathy is an uncommon macular injury caused by excessive light exposure. It is most often associated with direct sungazing or unsafe viewing of a solar eclipse, although similar retinal injury can occur after other intense photic exposures. It may also be referred to as solar maculopathy, eclipse retinopathy or photic retinopathy, depending on the clinical context.

Ophthalmology can feel completely new when you first encounter it at medical school. Compared with many other specialties, undergraduate exposure to ophthalmology can be relatively limited and varies considerably between medical schools. As a result, students may arrive on their first placement with little experience of ophthalmic examination or the equipment used in the specialty.

OSCEs are time-pressured environments where students are expected to recall examination sequences, communicate confidently with both patients and examiners, and accurately interpret their clinical findings. Ophthalmology can feel unfamiliar to medical students because the amount of undergraduate exposure varies considerably between medical schools. Ophthalmology teaching can also be limited within undergraduate curricula, leaving students and newly qualified doctors less confident in assessing common ophthalmic problems.

Oculomics is the study of ocular biomarkers as windows into systemic health. By combining high resolution retinal imaging with artificial intelligence (AI) analytics, the eye is moving from an organ specific diagnostic subject to something closer to a non-invasive systemic health sensor. A 2025 review in Progress in Retinal and Eye Research consolidated over a decade of this evidence, marking oculomics as a maturing field rather than an emerging one.

Congenital glaucoma is a rare but potentially blinding condition resulting from abnormal development of the aqueous outflow pathways, leading to elevated intraocular pressure (IOP) during infancy or early childhood. Although uncommon, it represents an important cause of irreversible childhood visual impairment if not diagnosed and treated promptly. The disorder encompasses a heterogeneous group of conditions with varying aetiologies, clinical presentations, and prognoses. This clinical overview summarises current literature on the epidemiology, pathophysiology, clinical features, diagnostic evaluation, and management of congenital glaucoma and highlights key principles relevant to clinical practice.

A 12-year-old girl with genetically confirmed Noonan syndrome presented five days after bilateral ptosis correction with a red, swollen right eye and two days of visual loss unresponsive to topical tobramycin. Visual acuity was counting fingers in the right eye and 0.8 (decimal) in the left. Both upper lids were oedematous with lagophthalmos, and the right eye showed chemosis, lower-lid ectropion and bulbar conjunctival exposure.

Artificial Intelligence (AI) has been increasingly improving in its accuracy and diagnostic performance in medicine in general. In the context of Keratoconus (KC), it has been heavily studied in the detection of established KC, with more recent evidence reports very high diagnostic accuracy, suggesting the value of machine learning algorithms in the management of KC (1–3). Much of the current discussion has been more focused on one question: Can AI diagnose accurately? A more important question may now be emerging: How can healthcare systems work alongside AI tools to risk stratify, plan management, and increase accessibility of care for patients that are identified as high risk?

During recent times, patients have been increasingly using large language models (LLM) when seeking medical advice, as a source of first line information before or after clinical review. The onus is put on the capability of these LLMs to provide patients with accurate and comprehensible information, such as effectively describing possible surgical options for the individuals conditions to recognising red flag symptoms where present. Validated instruments such as DISCERN and Flesch-Kincaid readability metrics offer a reproducible means of appraising such content. However, the quality and comprehensibility of LLM generated ptosis information has not yet been systematically evaluated. This study compares three widely used large language models (GPT-4o, Gemini 1.5 Pro, Claude 3.5 Sonnet) by using validated quality and readability metrics, examining whether meaningful differences exist between platforms in regards to the information they can provide on this common condition.

Thyroid eye disease (TED) is an autoimmune condition and it is the most prevalent extrathyroidal disorder of Grave’s disease and the course of this manifestation can vary from individuals leading to various complications with many treatment options ranging from the use of biologics to surgical interventions. The burden of this disorder can be unpredictable therefore it is important that patients understand what the disorder is and what complications to be aware of in order to receive the right treatment which can subsequently improve the outcomes both physically and cosmetically.