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What lies ahead: The promise of technology

Optometrist looking at optical coherence tomography (OCT) machine
Photo taken before COVID-19

Technology is an enabler of some of the changes we will see in the future.

In the last decade, optometry has already embraced technological developments such as fundus photography, leading to greater sophistication in assessment and diagnosis. Advances in imaging technology, such as optical coherence tomography (OCT) which takes high-resolution, cross-sectional images of the back of the eye, have enabled optometrists to spot changes in eye health at an earlier stage. OCT is becoming more and more commonplace in primary care optical practices, with many practice owners choosing to invest their own resources in this valuable equipment that would previously have only been used in hospitals.

We expect further significant technological changes in the next couple of decades, which could transform the role of the optometrist. The most significant one of these will be the use of artificial intelligence (AI) to diagnose eye disease. A partnership between Moorfields Eye Hospital and DeepMind Health has already shown that AI can help people to diagnose eye disease as accurately as world-leading experts.

To make a real difference, AI will need to be deployed in a way that makes efficient use of the whole eye healthcare workforce. Putting new AI tools in the hands of overstretched hospital consultants will bring some benefits – but there is a much bigger prize to be had.

We have already argued that primary eye healthcare providers in the UK – the optical practices familiar on every High Street which offer NHS and private sight tests – can play a major role in taking pressure off secondary eye care. Optometrists working in community practice are a skilled workforce, trained to detect eye diseases, and increasingly treating those diseases under NHS extended services contracts. And there are around ten registered optometrists for every ophthalmologist in the UK: they are a much more available workforce. But we recognise a limitation because most patients seen in High Street optical practices have healthy eyes. Primary care optometrists will, by definition, see rare conditions only rarely.

This is where AI could be a game-changer. A 2018 study25 demonstrated that the DeepMind model enables ophthalmology consultants to diagnose patients more accurately than when relying only on traditional tools, such as OCT, fundus exams and notes. It also indicated that if optometrists equipped with AI would obtain the same increase in diagnostic efficacy, they could routinely deliver specialist-level diagnoses in a primary care environment.

We have already seen a shift in the optometrists’ role: away from being refractionists who prescribe optical appliances to fully-fledged clinicians who interpret the findings of increasingly complex equipment. This requires the ability to explain technical information in lay terms, including sometimes the need to convey disappointing news to patients.

The large, widely distributed optometric workforce will then be able to play a much greater role in delivering eye care, reducing pressure on hospital eye services. To achieve that goal, policymakers will need to lay the foundations by:

  • Building the use of AI into optometrists’ training and professional development
  • Enhancing the role of the optometrist as “expert explainer”: interpreting and conveying the results of tests to patients, including imparting worrying news
  • Driving the design and commissioning of eye healthcare services in which primary optometrists routinely use AI to make quick and accurate diagnoses and referrals

AI will always be a tool for the optometrist rather than a replacement of their knowledge and interpretive skills. This is why optometric education needs to continue to provide a thorough grounding in theory alongside the practical skills they need.

Another key tool is the ability to refer patients to hospital electronically, including to transfer images and, crucially, to receive information about the outcome of the referral.

25De Fauw, J., Ledsam, J.R., Romera-Paredes, B. et al. Clinically applicable deep learning for diagnosis and referral in retinal disease. Nat Med 24, 1342–1350 (2018)