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Editorial
. 2022 Jun 1;12(2):121-122.
doi: 10.4103/tjo.tjo_23_22. eCollection 2022 Apr-Jun.

Current status of artificial intelligence for medicine

Hitoshi Tabuchi  1   2
Affiliations
Editorial

Current status of artificial intelligence for medicine

Hitoshi Tabuchi. Taiwan J Ophthalmol. .
No abstract available

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References

    1. Gulshan V, Peng L, Coram M, Stumpe MC, Wu D, Narayanaswamy A, et al. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA. 2016;316:2402–10. - PubMed
    1. Beede E, Baylor E, Hersch F, Iurchenko A, Wilcox L, Ruamviboonsuk P, et al. A Human-Centered Evaluation of a Deep Learning System Deployed in Clinics for the Detection of Diabetic Retinopathy. Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems. [Last acessed on 2021 Oct 17]. Available from: https://www.dl.acm.org/doi/abs/100.1145/3313831.3376718 .
    1. Ohsugi H, Tabuchi H, Enno H, Ishitobi N. Accuracy of deep learning, a machine-learning technology, using ultra-wide-field fundus ophthalmoscopy for detecting rhegmatogenous retinal detachment. Sci Rep. 2017;7:9425. - PMC - PubMed
    1. Buolamwini JA. Gender Shades: Intersectional Phenotypic and Demographic Evaluation of Face Datasets and Gender Classifiers (PhD Thesis). MIT. 2017. [Last accessed on 2021 Oct 17]. Available from: https://www.hdl:1721.1/114068.OCLC1026503582 .
    1. Masumoto H, Tabuchi H, Ohsugi H, Ishitobi N. Evaluation of Tigroid Fundus Discrimination Using Deep Neural Network. Japan Myopia Society Annual Meeting. 2018

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