Digital technology, tele-medicine and artificial intelligence in ophthalmology: A global perspective

Abstract

The simultaneous maturation of multiple digital and telecommunications technologies in 2020 has created an unprecedented opportunity for ophthalmology to adapt to new models of care using tele-health supported by digital innovations. These digital innovations include artificial intelligence (AI), 5th generation (5G) telecommunication networks and the Internet of Things (IoT), creating an inter-dependent ecosystem offering opportunities to develop new models of eye care addressing the challenges of COVID-19 and beyond. Ophthalmology has thrived in some of these areas partly due to its many image-based investigations. Tele-health and AI provide synchronous solutions to challenges facing ophthalmologists and healthcare providers worldwide. This article reviews how countries across the world have utilised these digital innovations to tackle diabetic retinopathy, retinopathy of prematurity, age-related macular degeneration, glaucoma, refractive error correction, cataract and other anterior segment disorders. The review summarises the digital strategies that countries are developing and discusses technologies that may increasingly enter the clinical workflow and processes of ophthalmologists. Furthermore as countries around the world have initiated a series of escalating containment and mitigation measures during the COVID-19 pandemic, the delivery of eye care services globally has been significantly impacted. As ophthalmic services adapt and form a "new normal", the rapid adoption of some of telehealth and digital innovation during the pandemic is also discussed. Finally, challenges for validation and clinical implementation are considered, as well as recommendations on future directions.

Keywords: Artificial intelligence; COVID-19; Deep learning; Diabetic retinopathy screening; Digital innovations; Digital technology; Digital transformation; Tele-ophthalmology; Tele-screening; Telemedicine.

Fig. 1

Customized image displacement onto functional retina by the Oculenz Augmented Reality Headset for patients with visual impairment from macular degeneration.

Fig. 2

Example of semi-automated remote triage workflow for medical retina.

Fig. 3

The role of AI in supporting tele-screening in DR, and the reciprocal contribution by tele-screening processes in improving AI algorithm performance and development of new capabilities.

Fig. 4

Streamlined patient journey with a single platform. Courtesy of Big Picture Eye Health.

Fig. 5

Illustration demonstrating how the Alleye™ application works. Courtesy of Alleye.

Fig. 6

Smart healthcare telemedicine service. Courtesy of Mr Peter Thomas. The dash box refers to automated pathway, which could proceed without an ophthalmologist reviewing the case and images. Example of ‘simple’ case: dry AMD diagnosed and recorded but no clinical action required and clinician oversight not required. Example of ‘complex case: macular hole potentially suitable for surgery, with clinician alerted and further clinical decision to be made.

Fig. 7

Example of semi-automated remote triage workflow for emergency ophthalmology.