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Beyond Predictions: Explainability and Learning from Machine Learning

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Digital Eye Care and Teleophthalmology

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Abstract

The intense interest in developing machine learning (ML) models for applications in ophthalmology has produced many potentially useful tools for disease detection, grading, and prognostication. However, though many of these efforts have produced well-validated models, the inner workings of these methods may not be easily understood by many clinicians, patients, and even ML practitioners. In this chapter, we focus on ML model explainability, and begin by first highlighting the utility and importance of explainability before presenting a clinician-accessible explanation of the commonly used methods and the type of insights these methods provide. Next, we present several case studies of ML studies incorporating explainability and describe these studies’ strengths as well as limitations. Finally, we discuss the important work that lies ahead, and how explainability may eventually help push the frontiers of scientific knowledge by enabling human experts to learn from what the machine has learned.

Chih-Ying Deng and Akinori Mitani contributed equally.

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Acknowledgements

Our sincere appreciation goes to Boris Babenko‬, Avinash Varadarajan, and Dale Webster for manuscript review and helpful discussions.‬‬‬‬‬‬‬‬‬‬‬‬‬

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  1. Google Health, 3400 Hillview Ave, Palo Alto, CA, 94304, USA

    Chih-Ying Deng, Akinori Mitani, Christina W. Chen, Lily H. Peng, Naama Hammel & Yun Liu

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  1. Chih-Ying Deng
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  2. Akinori Mitani
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  3. Christina W. Chen
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  4. Lily H. Peng
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Correspondence to Yun Liu .

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Editors and Affiliations

  1. School of Medicine, University of Notre Dame Australia, Fremantle, WA, Australia

    Kanagasingam Yogesan

  2. Department of Veterans Affairs, VA Palo Alto Health Care System, Palo Alto, CA, USA

    Leonard Goldschmidt

  3. Department of Optometry and Vision Science, University of California, Berkeley, CA, USA

    Jorge Cuadros

  4. Miller School of Medicine, Bascom Palmer Eye Institute University of Miami, Miami, FL, USA

    Giselle Ricur

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Deng, CY., Mitani, A., Chen, C.W., Peng, L.H., Hammel, N., Liu, Y. (2023). Beyond Predictions: Explainability and Learning from Machine Learning. In: Yogesan, K., Goldschmidt, L., Cuadros, J., Ricur, G. (eds) Digital Eye Care and Teleophthalmology. Springer, Cham. https://doi.org/10.1007/978-3-031-24052-2_15

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