Current status and practical considerations of artificial intelligence use in screening and diagnosing retinal diseases: Vision Academy retinal expert consensus

Yu-Bai Chou, Aditya U Kale, Paolo Lanzetta, Tariq Aslam, Jane Barratt, Carla Danese, Bora Eldem, Nicole Eter, Richard Gale, Jean-François Korobelnik, Igor Kozak, Xiaorong Li, Xiaoxin Li, Anat Loewenstein, Paisan Ruamviboonsuk, Taiji Sakamoto, Daniel S W Ting, Peter van Wijngaarden, Sebastian M Waldstein, David WongLihteh Wu, Miguel A Zapata, Javier Zarranz-Ventura

Research output: Contribution to journalReview articlepeer-review

Abstract

PURPOSE OF REVIEW: The application of artificial intelligence (AI) technologies in screening and diagnosing retinal diseases may play an important role in telemedicine and has potential to shape modern healthcare ecosystems, including within ophthalmology.

RECENT FINDINGS: In this article, we examine the latest publications relevant to AI in retinal disease and discuss the currently available algorithms. We summarize four key requirements underlining the successful application of AI algorithms in real-world practice: processing massive data; practicability of an AI model in ophthalmology; policy compliance and the regulatory environment; and balancing profit and cost when developing and maintaining AI models.

SUMMARY: The Vision Academy recognizes the advantages and disadvantages of AI-based technologies and gives insightful recommendations for future directions.

Original languageEnglish
Pages (from-to)403-413
Number of pages11
JournalCurrent opinion in ophthalmology
Volume34
Issue number5
DOIs
Publication statusPublished - 1 Sept 2023

Keywords

  • Humans
  • Artificial Intelligence
  • Consensus
  • Ecosystem
  • Algorithms
  • Retinal Diseases/diagnosis
  • retina
  • diagnosis
  • retinal imaging
  • artificial intelligence

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