AI & Oculomics
In conversation with Fares Antaki: The retinal age gap in schizophrenia
Fares Antaki. Nima Ghadiri (NG): The ‘retinal age gap’ (RAG) is a relatively new concept. Based on your study [1], how confident are you in proposing this as a non-invasive, accessible biomarker for accelerated biological ageing in patients with neuropsychiatric...
Artificial intelligence co-creation for educational learning tools and targeted simulators
Artificial intelligence (AI)-powered tools are becoming increasingly common within clinical practice and medical education, and using AI in simulation as a pure learning tool and for assessment has been widely discussed. Targeted, efficient use of such tools has been shown...
The next wave of AI in ophthalmology: From screening to communication
Artificial intelligence (AI) is reshaping ophthalmology, moving from research laboratories into everyday clinical care [1]. With its strong reliance on imaging and pattern recognition, ophthalmology is uniquely positioned to benefit from AI innovations. These developments range from autonomous disease screening...
Prognostic AI for diabetic retinopathy: Towards the first prospective trial in the UK
Artificial intelligence (AI) is frequently described as having the capacity to dramatically change and improve healthcare. One extensively studied application of AI in ophthalmology involves the diagnosis of diabetic retinopathy (DR) or diabetic maculopathy (DM) using retinal imaging. An emerging...
Ambient scribes: The silent revolution
Clinical practice is in the midst of a profound digital transformation with a new wave of technology gaining increasing prominence: ambient scribes. These AI-powered tools streamline documentation by converting doctor–patient conversations into structured clinical notes in near real time. Healthcare...
Regulatory approval for the use of AI as a medical device
A study led by researchers at Moorfields Eye Hospital and UCL Institute of Ophthalmology examined 36 ‘artificial intelligence as a medical device’ tools approved by regulators in Australia, Europe and the US, identifying that 19% had no published peer-reviewed data...
Developments in retinal pigmentation measurement and the hopes of an equitable future
Our AI & Oculomics co-editor, Nima Ghadiri, sat down with Abraham Olvera-Barrios from Moorfields and Anand Rajesh from the University of Washington to discuss their recent international study into retinal pigmentation and its wider clinical, technological and academic applications. Can...
Artificial intelligence and oculomics: Improving global health
The application of artificial intelligence (AI), and in particular deep learning, to high-resolution ocular imaging has led to many new discoveries, enabling the prediction of multiple different systemic diseases from ocular biomarkers. This emerging field is known as ‘oculomics’ [1]....
Identifying life-threatening uveal melanoma: A directed application of general-purpose AI
Uveal melanoma (UM) is a rare but aggressive eye cancer, affecting approximately six people per million annually [1]. Uveal melanoma arises in three locations: the choroid, ciliary body, and iris. As the most common primary intraocular malignancy in adults, UM...
Redefining healthcare through the eyes
The future of healthcare is being shaped by innovation in eyecare and in particular a field known as oculomics. This discipline leverages ocular biomarkers to provide insights into various health conditions, including cardiovascular diseases and psychological or neurological disorders [1]....
Strengthening the signal: Advancing oculomics research for systemic health insights
Oculomics, the study of how ocular structure reflects systemic health, is poised to become an integral tool for predicting, triaging, and diagnosing a wide range of diseases. By analysing data from the eye, particularly the retina, healthcare providers can gain...
Large language models in ophthalmology
Traditional artificial intelligence (AI) models typically require large amounts of labelled data for training. For example, to develop a model capable of detecting macular pathologies on optical coherence tomography scans, thousands of scans would need to be manually labelled by...
