Medical grade AI is booming, yet its impact comes only when models drive care and operations. This session will cover Samsung Medical Center's internally developed live tools and share pilot cases showing impact on both workforce and patients.

This presentation will explore the significant potential of AI and deep learning (DL) technologies in screening for diabetic retinal disease (DRD) through ophthalmic imaging modalities such as fundus photography (FP) and optical coherence tomography (OCT). I will share findings from our previous research, which demonstrated the robust performance of DL models in detecting referable diabetic retinopathy (DR) from FPs during external validation using datasets from Hong Kong. Moreover, I will present our development of a three-dimensional (3D) DL multi-task model that effectively identifies and classifies diabetic macular edema (DME) into centre-involved DME (CI-DME) or non-centre-involved DME (non-CI-DME), with excellent performance across various OCT devices and imaging protocols during external testing. Despite the promise of AI and DL technologies for DRD screening, their widespread implementation in Hong Kong faces several challenges. In this talk, I will discuss these challenges, including discrepancies between laboratory and real-world performance, the lack of transparency and explainability of current black-box AI models, and financial sustainability. I will also share our ongoing research efforts to generate evidence supporting the role of AI in DRD screening in real-world settings.

Artificial Intelligence (AI) is rapidly transforming various sectors, and the field of eye care is no exception. AI and ophthalmology have been a good marriage as the diagnosis and monitoring of most ophthalmological diseases are image dependent. Also, in the presence of these imaging modalities, image acquisition in ophthalmology is usually easy to perform, fast and non-invasive. As general practitioners have difficulty identifying ocular pathological conditions without adequate ophthalmic instruments or access to these imaging modalities, the referrals to ophthalmologists have surged in the past decades. The situation has been worsening with aging population and increase demand of early and accurate diagnosis of eye disorders. Overtime, there have been numerous sophisticated algorithms with significant advancements, evolving to not only interpret medical imaging with remarkable precision but also to assist in the accurate diagnosis of various eye conditions. This technological revolution is reshaping how eye diseases are diagnosed, treated, and managed, offering unprecedented opportunities for innovation and improvement especially in the future of ophthalmic care.