The DOI matches its Crossref record; 34 of the 34 references it cites were found in a registry or an index.
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.”
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]
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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.”
From Deep learning-enabled medical computer vision (2021) · cites this paper as [38]
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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].”
From Human-AI Collaboration via Conditional Delegation: A Case Study of Content Moderation (2022) · cites this paper as [9]
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Gulshan, V., Peng, L., Coram, M., Stumpe, M. C., Wu, D., Narayanaswamy, A., Venugopalan, S., Widner, K., Madams, T., Cuadros, J., et al. (2016) · JAMA · doi:10.1001/jama.2016.17216 · Cited by 7,697
Contextual Design: Defining Customer-Centered Systems
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Cai, C. J., Winter, S., Steiner, D., Wilcox, L., & Terry, M. (2019) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3359206 · Cited by 594
Self-citationElwyn, G. J., Scholl, I., Tietbohl, C. K., Mann, M. K., Edwards, A., Clay, C. F., Légaré, F., van der Weijden, T., Lewis, C. L., Wexler, R., et al. (2013) · BMC Medical Informatics and Decision Making · doi:10.1186/1472-6947-13-s2-s14 · Cited by 515
Diabetes patient education: a meta-analysis and meta-regression
Ellis, S. E., Speroff, T., Dittus, R. S., Brown, A. W., Pichert, J. W., & Elasy, T. A. (2003) · Patient Education and Counseling · doi:10.1016/s0738-3991(03)00016-8 · Cited by 492
A Review of 25 Years of CSCW Research in Healthcare: Contributions, Challenges and Future Agendas
Fitzpatrick, G., & Ellingsen, G. (2012) · Computer Supported Cooperative Work (CSCW) · doi:10.1007/s10606-012-9168-0 · Cited by 439
Human-Centered Tools for Coping with Imperfect Algorithms During Medical Decision-Making
Cai, C. J., Reif, E., Hegde, N., Hipp, J., Kim, B., Smilkov, D., Wattenberg, M., Viegas, F., Corrado, G. S., Stumpe, M. C., et al. (2019) · Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3290605.3300234 · Cited by 405
Participatory IT Design: Designing for Business and Workplace Realities
Bødker, K., Kensing, F., & Simonsen, J. (2004) · Cited by 346
Giger, M. L., Chan, H., & Boone, J. (2008) · Medical Physics · doi:10.1118/1.3013555 · Cited by 311
Research works printed but not linked: 3
- Kathleen Musante DeWalt and Billie R DeWalt. 2002. Participant Observation: A Guide for Fieldworkers. Rowman Altamira.
- Jenchitr W, Hanutsaha P, Iamsirithaworn S, Parnrat U, Choosri P. 2007. The national survey of blindness low vision and visual impairment in thailand 2006--2007. Thai J Pub Hlth Ophthalmol 21, 1 (2007), 10.
- Paisan Raumviboonsuk, Jonathan Krause, Peranut Chotcomwongse, Rory Sayres, Rajiv Raman, Kasumi Widner, Bilson JL Campana, Sonia Phene, Kornwipa Hemarat, Mongkol Tadarati, and others. 2019. Deep learning versus human graders for classifying diabetic retinopathy severity in a nationwide screening program. npj Digital Medicine 2, 1 (2019), 25.
Cited by
Sorted by citations
Rajpurkar, P., Chen, E., Banerjee, O., & Topol, E. J. (2022) · Nature Medicine · doi:10.1038/s41591-021-01614-0 · Cited by 3,212
Deep learning-enabled medical computer vision
Esteva, A., Chou, K., Yeung, S., Naik, N., Madani, A., Mottaghi, A., Liu, Y., Topol, E., Dean, J., & Socher, R. (2021) · npj Digital Medicine · doi:10.1038/s41746-020-00376-2 · Cited by 1,406
Toward Causal Representation Learning
Scholkopf, B., Locatello, F., Bauer, S., Ke, N. R., Kalchbrenner, N., Goyal, A., & Bengio, Y. (2021) · Proceedings of the IEEE · doi:10.1109/jproc.2021.3058954 · Cited by 1,170
The Many Faces of Robustness: A Critical Analysis of Out-of-Distribution Generalization
Hendrycks, D., Basart, S., Mu, N., Kadavath, S., Wang, F., Dorundo, E., Desai, R., Zhu, T., Parajuli, S., Guo, M., et al. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00823 · Cited by 1,250
Deep learning in histopathology: the path to the clinic
van der Laak, J., Litjens, G., & Ciompi, F. (2021) · Nature Medicine · doi:10.1038/s41591-021-01343-4 · Cited by 902
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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