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Review
. 2020 Sep;31(5):324-328.
doi: 10.1097/ICU.0000000000000694.

Controversies in artificial intelligence

T Y Alvin Liu  1 , Neil M Bressler
Affiliations
Review

Controversies in artificial intelligence

T Y Alvin Liu et al. Curr Opin Ophthalmol. 2020 Sep.

Abstract

Purpose of review: To review four recent controversial topics arising from deep learning applications in ophthalmology.

Recent findings: The controversies of four recent topics surrounding deep learning applications in ophthalmology are discussed, including the following: lack of explainability, limited generalizability, potential biases and protection of patient confidentiality in large-scale data transfer.

Summary: These controversial issues spanning the domains of clinical medicine, public health, computer science, ethics and legal issues, are complex and likely will benefit from an interdisciplinary approach if artificial intelligence in ophthalmology is to succeed over the next decade.

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References

    1. Burlina P, Pacheco KD, Joshi N, et al. Comparing humans and deep learning performance for grading AMD: a study in using universal deep features and transfer learning for automated AMD analysis. Comput Biol Med 2017; 82:80–86.
    1. Ting DSW, Cheung CY, Lim G, et al. Development and validation of a deep learning system for diabetic retinopathy and related eye diseases using retinal images from multiethnic populations with diabetes. JAMA 2017; 318:2211–2223.
    1. Burlina PM, Joshi N, Pekala M, et al. Automated grading of age-related macular degeneration from color fundus images using deep convolutional neural networks. JAMA Ophthalmol 2017; 135:1170–1176.
    1. Matsuba S, Tabuchi H, Ohsugi H, et al. Accuracy of ultra-wide-field fundus ophthalmoscopy-assisted deep learning, a machine-learning technology, for detecting age-related macular degeneration. Int Ophthalmol 2018; 39:1269–1275.
    1. Treder M, Lauermann JL, Eter N. Automated detection of exudative age-related macular degeneration in spectral domain optical coherence tomography using deep learning. Graefes Arch Clin Exp Ophthalmol 2018; 256:259–265.