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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Many empirical studies on human-AI decision-making did not observe complementary performance.
“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]
The “Challenges evaluating AI in clinical settings” section reports that mammography computer-aided detection generally did not improve radiologists’ diagnostic accuracy. That supports an example of human–AI assistance failing to improve performance, but this paper does not establish the broader claim that many empirical studies found no complementary performance, nor does it report a comparative performance result for its own deployment. Page numbers were not available in our copy. (LLM, conf=0.76)
Experimental — accuracy not yet measuredClinical usefulness of AI depends on calibration, interpretability, and integration into existing workflows.
“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]
The Discussion (page unavailable) says implementation is as important as algorithm accuracy and that clinical conditions affected use and performance; it also calls for reevaluating system thresholds. Expectations for AI-assisted screening (page unavailable) describes displaying images alongside predictions to help nurses use the results. These passages support workflow integration and aspects of making results understandable, but do not establish calibration and interpretability as the three stated requirements for clinical usefulness. (LLM, conf=0.82)
Experimental — accuracy not yet measuredDistrust challenges the adoption of machine learning-based CDSSs.
“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]
In “Consenting patients” (page not provided), the paper says nurses considered their trust in the deep learning system’s results when deciding whether to follow the study protocol. It does not establish that distrust challenged adoption of machine-learning CDSSs generally; the observed discouragement of participation was attributed primarily to concern about patient hardship. (LLM, conf=0.78)
Experimental — accuracy not yet measuredInvolving stakeholders is important for the design and evaluation of a system.
“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.”
From Understanding the Effect of Counterfactual Explanations on Trust and Reliance on AI for Human-AI Collaborative Clinical Decision Making (2023) · cites this paper as [8]
Adoption in clinical practice is influenced by clinician interaction with AI systems, including trust and workflow compatibility.
“Adoption in clinical practice is influenced by clinician interaction with AI systems, including trust and workflow compatibility [23,27].”
Regions of high-throughput healthcare, such as India and Thailand, have welcomed the deployment of technologies such as diabetic retinopathy screening systems.
“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]
When launching ML-powered diabetic retinopathy models in Thailand and India, researchers noticed that model performance was impacted by socioeconomic factors, and determined that where a model is most useful may not be where the model was generated.
“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.”
From Deep learning-enabled medical computer vision (2021) · cites this paper as [38]
Many studies have investigated the role of AI in assisting humans in making individual decisions.
“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]
AI performance can significantly drop for out-of-distribution examples that are different from the training data (in-distribution examples).
“AI performance can signifcantly drop for out-of-distribution examples that are diferent from the training data (in-distribution examples) [9,24,46,61].”
From Human-AI Collaboration via Conditional Delegation: A Case Study of Content Moderation (2022) · cites this paper as [9]
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“For healthcare professionals, researchers have also leveraged LLMs to support public health interventions [58], clinical pre-screening [48, 115], risk prediction [16, 42, 63, 86] and information processing [9, 10, 26, 64, 84, 85].”
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The Federated Tumor Segmentation (FeTS) ChallengePreprint
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Going deep into schizophrenia with artificial intelligence
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Nordic Human-Computer Interaction Conference
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- 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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