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Artificial Intelligence (AI) has attracted a huge amount of interest and excitement over the past 10 years. The most publicised application is in relation to language processing, for example as with ‘ChatGPT’, but other applications are almost infinite and include transportation and traffic management (including facilitating self-driving cars), increasing energy efficiency, aiding public safety, crime detection, facial recognition and object detection. AI also has widespread applications in the management of large datasets, analysis of data, identifying patterns, and data-driven diagnosis and decision-making.
It is in this latter context that AI is having a great impact in medicine and surgery – and especially in ophthalmology. Any medical specialty that uses digital imaging such as pathology, radiology and ophthalmology will be revolutionised by AI. In ophthalmology, as in other medical specialties, imaging has transformed diagnosis and patient management since the first X-Ray. Now, with CT and MRI scanning, ultrasonography, corneal imaging and retinal optical coherence tomography (OCT) to name just a few examples, being able to analyse very large datasets and utilise machine learning (ML) to improve diagnosis and management is a huge advantage for doctors and other health-care professionals. For example, with regard to corneal conditions such as keratoconus (see below scans), which can be difficult to diagnose in the early stages, AI can use information from very large databases with deep learning (DL) to improve diagnosis and predict which patient could be at risk of developing the condition in later life. A decision to treat the patient – or monitor closely can then be made.
These scans demonstrate keratoconus:
I have included 3 scientific review articles below (and in the reference section of this website) written by colleagues of mine at Moorfields Eye Hospital, London, in collaboration with scientists and physicians around the world, for those who might like to learn more about this exciting technology.