• Users Online: 183
  • Print this page
  • Email this page

Ahead of print publication  

Understanding required to consider artificial intelligence applications to the field of ophthalmology

 Department of Technology and Design Thinking for Medicine, Hiroshima University, Higashihiroshima, Japan

Date of Submission18-Jun-2022
Date of Acceptance19-Jul-2022
Date of Web Publication22-Sep-2022

Correspondence Address:
Hitoshi Tabuchi,
Department of Technology and Design Thinking for Medicine, Hiroshima University, Higashihiroshima
Login to access the Email id

Source of Support: None, Conflict of Interest: None

DOI: 10.4103/2211-5056.356685

How to cite this URL:
Tabuchi H. Understanding required to consider artificial intelligence applications to the field of ophthalmology. Taiwan J Ophthalmol [Epub ahead of print] [cited 2023 Mar 23]. Available from: https://www.e-tjo.org/preprintarticle.asp?id=356685

Dear Editor,

The reviewer's thought-provoking comments on our review article are appreciated. Based on these comments, information on state-of-the-art applications in artificial intelligence has been added to the text.

It is recognized that poor performance in geographically different populations (facilities) is a typical example of evaluating the data that differs from the training dataset.

There are two events where artificial intelligence (AI) can be implemented to evaluate the data from unlearned groups: Current events and future events.

Current events:

  • Differences in equipment (e.g., different versions of medical equipment and different manufacturers of medical equipment)
  • Differences in medical personnel (e.g., the skill level of photographers and the skill level of technicians who complete staining in pathology)
  • Regional differences (e.g., different races and, in the case of pathology, different pH levels of the water).

Future events:

  • All ongoing events
  • Shifts in patient distribution (e.g., immigration, aging population)
  • A shift in criteria (e.g., change in the medical criteria for determining any disease).

We can handle current events if we invest time and effort. However, future events will occur after the machine learning model is completed, and they are even more challenging to predict. Therefore, the performance of the machine learning model deteriorates over time if improvements are not made.[1]

Machine learning operations (MLOps)[2] are becoming increasingly important and efforts should be made in this area. From an MLOps perspective, it is essential to have a mechanism to maintain and perpetuate the performance of artificial intelligence after its implementation in society. For example, for the intraocular lens check AI that the author and his team are developing,[3] providers must continuously replace the training data from obsolete intraocular lenses with new data.

However, as mentioned in the main text, there are objections to changes in the performance of an application certified as a medical device, for better or worse.

We would like to thank the reviewer for sharing a fascinating paper on 3D technology applications. Our team has previously published an AI application for diagnosing ERM using 3D OCT images,[4] and we are continuing unpublished research in clinical applications combining 3D images and AI technology.

Financial support and sponsorship


Conflicts of interest

The author declares that there are no conflicts of interests of this paper.

  References Top

Cao T, Huang CW, Yu-Tung Hui D, Cohen JP. A benchmark of medical out of distribution detection. arXiv 2007. [doi: 10.48550/arXiv. 2007.04250].  Back to cited text no. 1
Breuel C. “ML Ops: Machine Learning as an Engineering Discipline”. Towards Data Science. Available from: https://towardsdatascience.com/ml-ops-machine-learning-as-an-engineering- discipline- b86ca4874a3f. [Last accessed on 2022 Jun 18].  Back to cited text no. 2
Tabuchi H, Masumoto H, Adachi S. Real-world testing of artificial intelligence system for surgical safety management. Invest Ophthalmol Vis Sci 2020;61:2032.  Back to cited text no. 3
Sonobe T, Tabuchi H, Ohsugi H, Masumoto H, Ishitobi N, Morita S, et al. Comparison between support vector machine and deep learning, machine-learning technologies for detecting epiretinal membrane using 3D-OCT. Int Ophthalmol 2019;39:1871-7.  Back to cited text no. 4


     Search Pubmed for
    -  Tabuchi H
    Access Statistics
    Email Alert *
    Add to My List *
* Registration required (free)  

  In this article

 Article Access Statistics
    PDF Downloaded9    

Recommend this journal