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A Human-Centered Evaluation of a Deep Learning System Deployed in Clinics for the Detection of Diabetic Retinopathy
Open accessAbstract
Deep learning algorithms promise to improve clinician workflows and patient outcomes. However, these gains have yet to be fully demonstrated in real world clinical settings. In this paper, we describe a human-centered study of a deep learning system used in clinics for the detection of diabetic eye disease. From interviews and observation across eleven clinics in Thailand, we characterize current eye-screening workflows, user expectations for an AI-assisted screening process, and post-deployment experiences. Our findings indicate that several socio-environmental factors impact model performance, nursing workflows, and the patient experience. We draw on these findings to reflect on the value of conducting human-centered evaluative research alongside prospective evaluations of model accuracy.
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“However, many empirical studies on human-AI decision-making did not observe complementary performance [6,16,37,52,58,61,68,82,84,87].”
From Understanding the Role of Human Intuition on Reliance in Human-AI Decision-Making with Explanations (2023) · cites this paper as [6]
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“Clinical usefulness depends on calibration, interpretability, and integration into existing workflows [23,24].”
From Artificial Intelligence in Clinical Decision-Making: A Comprehensive Review of Diagnostic, Prognostic, and Therapeutic Applications, Validation Gaps, Bias, and Deployment Challenges (2026) · cites this paper as [23]
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“Such distrust has been shown to challenge the adoption of machine learning–based CDSSs [7-10]”
From Factors influencing clinician trust in predictive clinical decision support systems for in-hospital deterioration: qualitative descriptive study (2022) · cites this paper as [8]
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“there has been increasing recent research efforts [8, 15, 22, 41, 58, 65] that highlight the importance of involving stakeholders to understand their practices and needs [15, 41, 65] and socio-environmental factors [8] for the design and evaluation of a system.”
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“Adoption in clinical practice is influenced by clinician interaction with AI systems, including trust and workflow compatibility [23,27].”
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“For instance, regions of high-throughput healthcare, such as India and Thailand, have welcomed the deployment of technologies such as diabetic retinopathy screening systems 38 .”
From Deep learning-enabled medical computer vision (2021) · cites this paper as [38]
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“For example, when launching ML-powered diabetic retinopathy models in Thailand and India, researchers noticed that model performance was impacted by socioeconomic factors 38 , and determined that where a model is most useful may not be where the model was generated.”
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“Many studies have investigated the role of AI in assisting humans in making individual decisions [9,16,36, 37, 50, 51, 55, 58, 69, 83, 84, 89], e.g., predicting whether a person will recidivate in the near future.”
From Human-AI Collaboration via Conditional Delegation: A Case Study of Content Moderation (2022) · cites this paper as [9]
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“AI performance can signifcantly drop for out-of-distribution examples that are diferent from the training data (in-distribution examples) [9,24,46,61].”
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Metadata
- Authors
- Beede, E., Baylor, E., Hersch, F., Iurchenko, A., Wilcox, L., Ruamviboonsuk, P., & Vardoulakis, L. M.
- Affiliations
- Google (United States) (US)
- Google (Singapore) (SG)
- Rajavithi Hospital (TH)
- Venue
- Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems
- Publisher
- ACM
- Published
- 2020-04-21
- Pages
- 1-12
- OpenAlex
- W3004483087
- Cited by
- 614
- References
- 34
- Citations by year
- 2026: 85 · 2025: 115 · 2024: 104 · 2023: 89 · 2022: 97 · 2021: 94 · 2020: 28
- FWCI
- 39.56
- Type
- conference paper
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- CHI Conference on Human Factors in Computing Systems (publishedVersion)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Retinal Imaging and Analysis
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- diabetic retinopathy detection, human-centered evaluation, AI-assisted screening, nursing workflow, patient experience, socio-environmental factors
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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