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Clinician-driven artificial intelligence in ophthalmology: resources enabling democratization

Korot, E; Gonçalves, MB; Khan, SM; Struyven, R; Wagner, SK; Keane, PA; (2021) Clinician-driven artificial intelligence in ophthalmology: resources enabling democratization. Current Opinion in Ophthalmology , 32 (5) pp. 445-451. 10.1097/ICU.0000000000000785. Green open access

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Abstract

PURPOSE OF REVIEW: This article aims to discuss the current state of resources enabling the democratization of artificial intelligence (AI) in ophthalmology. RECENT FINDINGS: Open datasets, efficient labeling techniques, code-free automated machine learning (AutoML) and cloud-based platforms for deployment are resources that enable clinicians with scarce resources to drive their own AI projects. SUMMARY: Clinicians are the use-case experts who are best suited to drive AI projects tackling patient-relevant outcome measures. Taken together, open datasets, efficient labeling techniques, code-free AutoML and cloud platforms break the barriers for clinician-driven AI. As AI becomes increasingly democratized through such tools, clinicians and patients stand to benefit greatly.

Type: Article
Title: Clinician-driven artificial intelligence in ophthalmology: resources enabling democratization
Location: United States
Open access status: An open access version is available from UCL Discovery
DOI: 10.1097/ICU.0000000000000785
Publisher version: https://doi.org/10.1097/ICU.0000000000000785
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > Institute of Ophthalmology
URI: https://discovery.ucl.ac.uk/id/eprint/10132141
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