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Ophthalmology: A pioneer in the field of artificial intelligence

Digital EditionOphthalmology Times: June 1, 2021
Volume 46
Issue 09

Retinal imaging tests are providing material to train and test decision-support systems.

Reviewed by Konstantinos Balaskas, MD, FEBO, MRCOphth

Ophthalmology, with its heavy reliance on imaging, is an innovator in the field of artificial intelligence (AI) in medicine.

Although the opportunities for patients and health care professionals are great, hurdles to fully integrating AI remain, including economic, ethical, and data-privacy issues.

Deep learning
According to Konstantinos Balaskas, MD, FEBO, MRCOphth, a retinal expert at Moorfields Eye Hospital, London, United Kingdom, and director of the Moorfields Ophthalmic Reading Centre and AI Analytics Hub, AI is a broad term.

Related: Cutting-edge neuro-ophthalmology: Combining artificial intelligence, eye tracking

“The type of AI that has generated a lot of excitement in recent years is called ‘deep learning,’ ” he said. “This is a process by which software programs learn to perform certain tasks by processing large quantities of data.”

Deep learning is what has made ophthalmology a pioneer in the field of implementing AI in medicine, because we are increasingly reliant on imaging tests to monitor our patients.

“Particularly in my subspecialty of interest, medical retina, imaging tests such as optical coherence tomography (OCT) are performed very frequently and have provided the material to train, test, and then apply AI decision support systems,” Balaskas noted.

In retina particularly, some of the most common causes of visual loss in the Western world—such as age-related macular degeneration (AMD) and diabetic retinopathy—require early detection, prompt initiation of treatment, and regular monitoring to preserve vision.

Balaskas said this is where AI decision support systems can help improve access to care and ensure optimal clinical outcomes for patients.

Balaskas cited the AI decision support system developed in collaboration between Moorfields Eye Hospital, where he is based, and Google DeepMind.

Related: Blockchain technology aims to drive big data to 'train' AI

“It is able to read OCT scans, interpret them, provide a diagnosis, and make management recommendations,” he said. “The other area where AI shows promise is in the development of personalized treatment plans for patients by being able to predict their response to treatment and their visual outcomes over a period of time.”

Support tools
When considering common conditions that threaten vision, such as AMD and diabetic retinopathy, Balaskas says AI decision support tools—once validated and once they have gained regulatory approval as medical devices—can help improve access to care.

“They can, for example, assist health practitioners in the community in diagnosing diseases early,” he explained. “In the United Kingdom, where OCT scans are widely available in high street optician practices, an AI tool would be particularly useful to assist them to interpret scans correctly and identify disease at an early stage.”

Similarly, in diabetic retinopathy, where patients require regular screening and monitoring, AI tools can significantly increase efficiency of screening programs.

Balaskas pointed out that such applications already exist and can be of particular use in diabetic retinopathy screening programs such as in underresourced health care settings.

“Other indications for the application of AI monitoring, like AMD, are in advanced stages of development but have not yet been implemented in real life,” he added.

Balaskas said there are challenges with integrating AI into retina diagnostics and treatments.

Related: Integrating AI to manage diabetic retinopathy in a primary care setting

He noted that he has a personal academic interest in implementation science, which looks at the gap between developing a medical device such as an AI decision support tool and deploying it in clinical practice.1

“The potential barriers that we need to overcome for the tool to be deployed in a meaningful way to improve outcomes for our patients go beyond testing and validation,” he said. “These include economic evaluations: how would such an automated decision support model affect the finances of a health care system, so that it could provide good value for money or achieve cost savings?”

Human factors
The next consideration is human factors, particularly how these models of care that rely on AI are perceived and accepted by patients and practitioners.

What is the level of trust in these technologies? What level of information and education of patients and the public is required to build confidence in their use? Then there are considerations regarding training and technical infrastructure to support these tools.

Balaskas notes that ethical and data-privacy issues, as well as medicolegal considerations, are also important. Who is responsible for decisions made by an AI algorithm rather than a human? How do these tools affect the way health care professionals diagnose and treat disease?

Related: Deep learning algorithm proven accurate for AMD classification

“There is a phenomenon called automation bias, where practitioners are sometimes more likely to defer to the recommendation of the AI tool—even perhaps against their better judgement,” he said.

Balaskas notes the issue of interpretability—that in many instances these AI tools are opaque in their functioning.

“We do not fully understand how a specific recommendation is reached, whether that is a diagnosis or a management recommendation, and that lack of transparency can exacerbate the medical, legal, and ethical issues that were mentioned earlier,” he pointed out. “In summary, we have found that there are several hurdles to overcome before AI tools can be deployed in real life in a way that is safe and will improve clinical outcomes.”

Moreover, Balaskas said that life could change for ophthalmologists in the future, and he has a optimistic vision of AI in medical practice.

“Our field is becoming increasingly complex and we need to process data from various sources when we are assessing our patients: data from the many imaging modalities, genetic data and the various types of omics, such as proteomics and the emerging field of oculomics, where features on the eye examination can be indicative of problems with systemic health,” he said.

Related: Telemedicine ushers in new chapter in eye care

Balaskas also noted that data from home vision monitoring devices will become increasingly available.

However, Balaskas said that making sense of all this data in order to develop a personalized treatment plan for each patient can be daunting.

“AI could become a very useful aid and, as described in the Topol Review on AI commissioned by Health Education England, provide the gift of time to patients and practitioners, giving them the chance to discuss and decide together what the optimal treatment plan is, informed by the processing of high-dimensional complex data sources,” he concluded.


Konstantinos Balaskas, MD, FEBO, MRCOphth

Balaskas has an academic interest in new ways of delivering care in ophthalmology, including telemedicine, virtual clinics, remote monitoring, and AI decision support. He has no financial disclosures.


1. Campbell JP, Mathenge C, Cherwek H, et al; American Academy of Ophthalmology Task Force on Artificial Intelligence. Artificial intelligence to reduce ocular health disparities: moving from concept to implementation. Transl Vis Sci Technol. 2021;10(3):19. doi:10.1167/tvst.10.3.19

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