Introducing the AI directory
This is a free and useful resource that the College has created for clinicians, commissioners, vendors, researchers and anyone with an interest in artificial intelligence (AI).
We have developed the directory because we believe that AI has the potential to have a profound impact on the science and practice of ophthalmology.
Comprising information published by vendors and academic sources, the AI directory shows at-a-glance artificial intelligence as a medical device (AIaMD) tools that have contributed to the specialty in the UK. It does not recommend specific vendors or their products, or draw any comparisons between devices.*
Why do we need an AI directory?
The use and influence of AI will continue to expand as the technology develops and more AIaMD tools are adopted for clinical, research and administrative use. Our AI directory will capture this development and growth. Currently, no AIaMD tools have been rolled out across NHS ophthalmology departments: their use is localised or has contributed to studies and research, and some tools have been tested in pilot form. It is strongly anticipated that in time devices will become patient-facing.
How will ophthalmology lead the way in AI adoption?
Ophthalmology is an image-focused specialty, and so AI is playing an increasingly important role in diagnostics. Datasets such as multiple retinal scans that ophthalmologists rely on for making a diagnosis and developing a treatment plan readily lend themselves to the training of AI algorithms.
Addressing concerns about the role of AI in ophthalmology
As we outline in our AI position statement, AI will not replace doctors, but time-saving tools support efficiency improvements. Globally, AI has the potential to offer health providers scalable solutions for screening and diagnosis, which should speed up patient waiting times and streamline referral to treatment pathways. However, AIaMD tools should be developed, validated and deployed across a fully diverse patient population to ensure that their use in clinical and diagnostic settings minimises the risk of exacerbating health inequities. Clear patient guidelines on data usage and sharing are also essential.