Abstract
Accurate and efficient triaging to ophthalmology services is essential to patient care and appropriate resource allocation. Current triaging processes are both time consuming and prone to human error. The use of deep learning (DL) and natural language processing (NLP) in ophthalmology triaging is a novel application of artificial intelligence (AI) established at the South Australian Institute of Ophthalmology (SAIO), Australia. AI assisted triaging has demonstrated early promise in the ability to identify urgent referrals with potential sight-threatening pathologies, with accuracies of up to 81%. Technical challenges in AI assisted triaging include small dataset size, distant labels and the presence of specialized medical vocabulary. Future research relating to AI assisted triaging should endeavour to use larger sample sizes, specialist guided triage allocation, and data from multiple centres.
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Tan, Y., Bacchi, S., Chan, W.O. (2021). Artificial Intelligence in Ophthalmology Triaging. In: Grzybowski, A. (eds) Artificial Intelligence in Ophthalmology. Springer, Cham. https://doi.org/10.1007/978-3-030-78601-4_19
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DOI: https://doi.org/10.1007/978-3-030-78601-4_19
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