Journal of Shandong University (Health Sciences) ›› 2020, Vol. 58 ›› Issue (11): 1-10.doi: 10.6040/j.issn.1671-7554.0.2020.1181

• Special topic on new progress in ophthalmic artificial intelligence •     Next Articles

Applying artificial intelligence in ophthalmic real-world practice: opportunities and challenges

Mingguang HE1,*(),Chi LIU1,2,Zhixi LI1   

  1. 1. State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou 510060, Guangdong, China
    2. School of Computer Science, University of Technology Sydney, Sydney 2007, NSW, Australia
  • Received:2020-08-17 Online:2020-11-10 Published:2020-11-04
  • Contact: Mingguang HE E-mail:mingguang_he@yahoo.com

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Abstract:

Artificial intelligence has rapidly evolved from the experimental phase to the implementation phase in many clinical disciplines, including ophthalmology. The data-driven deep learning technology has created unprecedented opportunities for major breakthroughs in the imaging data-based automated diagnoses in ophthalmology, significantly improving the accessibility, efficiency, and cost-effectiveness of eye care systems. Although this technology will have a profound impact on clinical flow and practice patterns sooner or later, translating such a technology into clinical practice is challenging. With comprehensively going through the latest progress in this research domain, this article highlights the opportunities and challenges of the real-world deployment of artificial intelligence in ophthalmology, and figures out the potential problems that may arise during the transition, such as diagnosis bias, clinical evaluation, medical accountability, as well as ethical and legal issues. The discovery could facilitate the integration of artificial intelligence into routine clinical practice and further improve the relevant applications.

Key words: Artificial intelligence, Ophthalmology, Real-world deployment, Clinical practice

CLC Number: 

  • R770.4

Fig.1

Artificial intelligence triage opportunistic screening model"

Fig.2

Artificial intelligence as an initial triage strategy to maximize efficiency of manual grading"

Fig.3

Three models of integration: triage, add-on, and replacement"

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