A Human-Centered Evaluation of a Deep Learning System Deployed in Clinics for the Detection of Diabetic Retinopathy

Beede. (2020). 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.

Type

Conference paper

Intellectual base

Primary research

Year

2020

DOI

10.1145/3313831.3376718

DOI registrant

Association for Computing Machinery

Link

https://doi.org/10.1145/3313831.3376718

Cited by

1 citation across 1 artifact.

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Domain-specific

• Covariate-Shift Blind Spot
  • Google Health's diabetic-retinopathy screening system is a clean defining case (Beede et al., CHI 2020, "A Human-Centered Evaluation of a Deep Learning System Deployed in Clinics for the Detection of Diabetic Retinopathy").

    This sourceHuman-centred field study of a deep learning system deployed in clinics for diabetic eye disease detection, based on interviews about its real-world use. Field evaluation of a deployed deep learning system for diabetic eye disease, finding that real-world clinical performance falls short of laboratory promise. Human-centred evaluation reporting that the deployed diabetic-retinopathy system met real-world difficulties that undercut its laboratory promise, including its handling of sub-quality images.

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