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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]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
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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].”
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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
Beyer, H. R., & Holtzblatt, K. (1997) · Cited by 2,748
Abràmoff, M. D., Lavin, P. T., Birch, M., Shah, N., & Folk, J. C. (2018) · npj Digital Medicine · doi:10.1038/s41746-018-0040-6 · Cited by 1,638
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
Flagged first, then by citations
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.
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Liu, L., Wu, X., Lin, D., Zhao, L., Li, M., Yun, D., Lin, Z., Pang, J., Li, L., Wu, Y., et al. (2023) · Cell Reports Medicine · doi:10.1016/j.xcrm.2022.100912 · Cited by 26
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Heiliger, L., Sekuboyina, A., Menze, B. H., Egger, J., & Kleesiek, J. (2022) · doi:10.36227/techrxiv.19103432.v1 · Cited by 28
Barnard, L., Botha, H., Corriveau-Lecavalier, N., Graff-Radford, J., Dicks, E., Gogineni, V., Zhang, G., Burkett, B. J., Johnson, D. R., Huls, S. J., et al. (2025) · Neurology · doi:10.1212/wnl.0000000000213831 · Cited by 25
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Jovy-Klein, F., Stead, S., Salge, T. O., Sander, J., Diehl, A., & Antons, D. (2024) · BMC Health Services Research · doi:10.1186/s12913-024-11895-z · Cited by 27
Nizamani, M. M., Zhang, H.-L., & Lai, Z. (2025) · Technology in Society · doi:10.1016/j.techsoc.2025.103121 · Cited by 25
Lindvall, M., Lundström, C. F., & Löwgren, J. (2021) · ACM International Conference on Intelligent User Interfaces (IUI) · doi:10.1145/3397481.3450681 · Cited by 33
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Papenmeier, A., Kern, D., Hienert, D., Kammerer, Y., & Seifert, C. (2022) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3491102.3501915 · Cited by 29
Chuter, B., Huynh, J., Bowd, C., Walker, E., Rezapour, J., Brye, N., Belghith, A., Fazio, M. A., Girkin, C. A., Moraes, G. D., et al. (2024) · Translational Vision Science & Technology · doi:10.1167/tvst.13.1.23 · Cited by 25
McCoy, L. G., Bihorac, A., Celi, L. A., Elmore, M., Kewalramani, D., Kwaga, T., Martinez‐Martin, N., Prôa, R., Schamroth, J., Shaffer, J. D., et al. (2025) · BMC Global and Public Health · doi:10.1186/s44263-025-00158-6 · Cited by 24
Duggal, M., Chauhan, A., Gupta, V., Kankaria, A., Budhija, D., Verma, P., Miglani, V., Syal, P., Kaur, G., Kumar, L., et al. (2025) · JMIR Medical Informatics · doi:10.2196/67529 · Cited by 24
Astbury, S., Grove, J. I., Dorward, D. A., Guha, I. N., Fallowfield, J. A., & Kendall, T. J. (2021) · The Journal of Pathology Clinical Research · doi:10.1002/cjp2.227 · Cited by 24
Noriega-Campero, A., Meizner, D., Camacho‐García‐Formentí, D., Enciso, J., Quiroz-Mercado, H., Morales-Cantón, V., Almaatouq, A., & Pentland, A. (2021) · JMIR Formative Research · doi:10.2196/25290 · Cited by 23
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Maalej, W., Pham, Y. D., & Chazette, L. (2023) · Computer · doi:10.1109/mc.2023.3243182 · Cited by 23
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Jones, D. T., & Kerber, K. A. (2022) · Neurology · doi:10.1212/wnl.0000000000013200 · Cited by 25
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Steppan, J., & Hanke, S. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2101.03814 · Cited by 28
Rodríguez, A., Arruabarrena, C., Allendes, G., Olivera, M., Zarranz‐Ventura, J., & Teus, M. A. (2024) · Scientific Reports · doi:10.1038/s41598-024-68489-2 · Cited by 22
Zenk, M., Zimmerer, D., Isensee, F., Traub, J., Norajitra, T., Jäger, P. F., & Maier-Hein, K. H. (2024) · Medical Image Analysis · doi:10.1016/j.media.2024.103392 · Cited by 22
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Qazi, M. A., Hashmi, A. U. R., Sanjeev, S., Almakky, I., Saeed, N., González, C., & Yaqub, M. K. (2025) · ACM Computing Surveys · doi:10.1145/3785663 · Cited by 29
Wen, J., Liu, D., Wu, Q., Zhao, L., Iao, W. C., & Lin, H. (2023) · Open Research (University of Surrey) · doi:10.1002/viw.20220070 · Cited by 23
Janmohamed, M., Nhu, D., Kuhlmann, L., Gilligan, A. K., Tan, C. W., Perucca, P., O'Brien, T. J., & Kwan, P. (2022) · Brain Communications · doi:10.1093/braincomms/fcac218 · Cited by 24
Agapie, E., Haldar, S., & Poblete, S. G. (2022) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3555610 · Cited by 21
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Okolo, C. T., Agarwal, D., Dell, N. L., & Vashistha, A. (2024) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3637348 · Cited by 28
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Ulloa, M., Rothrock, B., Ahmad, F. S., & Jacobs, M. L. (2022) · Frontiers in Computer Science · doi:10.3389/fcomp.2022.1045704 · Cited by 24
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Yuen, V. L., Ran, A. R., Shi, J., Sham, K., Yang, D., Chan, V. T. T., Chan, R. N. C., Yam, J. C. S., Tham, C. C. Y., McKay, G. J., et al. (2021) · Translational Vision Science & Technology · doi:10.1167/tvst.10.11.16 · Cited by 20
Kannampallil, T., Dai, R., Lv, N., Xiao, L., Lu, C., Ajilore, O., Snowden, M. B., Venditti, E. M., Williams, L. M., Kringle, E. A., et al. (2022) · Journal of Affective Disorders · doi:10.1016/j.jad.2022.04.015 · Cited by 20
Yang, F., Yan, D., & Wang, Z. (2024) · Journal of bone oncology · doi:10.1016/j.jbo.2024.100525 · Cited by 20
Lee, M. H., Siewiorek, D. P., Smailagic, A., Bernardino, A., & Bermúdez i Badia, S. (2022) · 27th International Conference on Intelligent User Interfaces · doi:10.1145/3490099.3511112 · Cited by 19
“In addition, there is increasing recent research efforts [6, 13, 21, 40, 54, 60] that highlight the importance of involving stakeholders to understand their practices and needs [13, 40, 60] and socio-environmental factors [6] for the design and evaluation of a system.”
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Zhang, Z. T., Argın, S. K., Bilen, M. B., Urgun, D., Deniz, S. M., Liu, Y., & Hassib, M. E. (2024) · Behaviour and Information Technology · doi:10.1080/0144929x.2024.2431055 · Cited by 22
Zenk, M., Baid, U., Pati, S., Linardos, A., Edwards, B., Sheller, M., Foley, P., Aristizábal, A., Zimmerer, D., Gruzdev, A. D., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-60466-1 · Cited by 29
Niazi, S. K. (2026) · Journal of Chemistry · doi:10.1155/joch/5202999 · Cited by 19
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White, T., Selvarajah, V., Sand, F. W., Svangård, N., Mohankumar, G., Fenici, P., Rough, K., Onyango, N. O., Lyons, K., Mack, C. D., et al. (2024) · Diabetes Obesity and Metabolism · doi:10.1111/dom.15587 · Cited by 21
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Cai, L., & Pfob, A. (2024) · Abdominal Radiology · doi:10.1007/s00261-024-04640-x · Cited by 19
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Andersen, J. S., & Maalej, W. (2023) · IEEE Software · doi:10.1109/ms.2023.3340256 · Cited by 21
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Domalpally, A., & Channa, R. (2021) · The Lancet Digital Health · doi:10.1016/s2589-7500(21)00140-0 · Cited by 18
Wu, S., Cao, W., Fu, S., Yao, B., Yang, Z., Yin, C., Mishra, V., Addison, D., Zhang, P., & Wang, D. (2025) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3714272 · Cited by 22
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Azad, T. D., Krumholz, H. M., & Saria, S. (2026) · Nature Medicine · doi:10.1038/s41591-025-04198-1 · Cited by 17
Zeng, M., Oakden‐Rayner, L., Bird, A., Smith, L., Wu, Z., Scroop, R., Kleinig, T. J., Jannes, J., Jenkinson, M., & Palmer, L. J. (2022) · Frontiers in Neurology · doi:10.3389/fneur.2022.945813 · Cited by 17
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Rees, N., Holding, K., & Sujan, M. A. (2023) · Frontiers in Computer Science · doi:10.3389/fcomp.2023.1134818 · Cited by 17
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Mendel, T., Nov, O., & Wiesenfeld, B. M. (2024) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3686925 · Cited by 19
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Ganguli, I., Gordon, W. J., Lupo, C., Sands-Lincoln, M., George, J., Jackson, G. P., Rhee, K. B. L., & Bates, D. W. (2020) · NEJM Catalyst · doi:10.1056/cat.20.0094 · Cited by 17
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Tran, T., du Toit, C., & Padmanabhan, S. (2021) · Journal of Hospital Management and Health Policy · doi:10.21037/jhmhp-20-132 · Cited by 17
Wang, Y., Shi, D., Tan, Z., Niu, Y. Y., Jiang, Y., Xiong, R., Peng, G., & He, M. (2021) · Frontiers in Medicine · doi:10.3389/fmed.2021.740987 · Cited by 17
Achievements and Challenges in Explaining Deep Learning based Computer-Aided Diagnosis SystemsPreprint
Lucieri, A., Bajwa, M. N., Dengel, A. R., & Ahmed, S. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2011.13169 · Cited by 20
Liu, D., Zhang, Y., Zhao, B., Ma, S., Shi, C., & Ma, X. (2025) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3714196 · Cited by 22
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Liu, H., Li, H., Wang, X., Li, H., Ou, M., Hao, L., Hu, Y., & Liu, J. (2022) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc48229.2022.9871507 · Cited by 16
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Verma, A. A., Trbovich, P., Mamdani, M., & Shojania, K. G. (2023) · BMJ Quality & Safety · doi:10.1136/bmjqs-2022-015713 · Cited by 17
van der Vegt, A. H., Campbell, V. K., & Zuccon, G. (2023) · The Medical Journal of Australia · doi:10.5694/mja2.52195 · Cited by 17
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Herrmann, T. A. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-05643-7_3 · Cited by 16
Zhang‐James, Y., Buitelaar, J. K., The ENIGMA‐ASD Working Group, van Rooij, D., & Faraone, S. V. (2021) · JCPP Advances · doi:10.1002/jcv2.12042 · Cited by 17
Hong, J. C., Eclov, N. C. W., Stephens, S. J., Mowery, Y. M., & Palta, M. (2022) · BMC Bioinformatics · doi:10.1186/s12859-022-04940-3 · Cited by 15
Krones, F., & Walker, B. (2024) · PLOS Digital Health · doi:10.1371/journal.pdig.0000437 · Cited by 17
Kahr, P. K., Rooks, G., Snijders, C. C. P., & Willemsen, M. C. (2025) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3713099 · Cited by 19
Yuen, J., Pike, S., Khachikyan, S., & Nallasamy, S. (2022) · Exon Publications eBooks · doi:10.36255/exon-publications-digital-health-telehealth-ophthalmology · Cited by 15
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Shad, R., Zakka, C., Kaur, D., Mathur, M., Fong, R., Cho, J., Filice, R. W., Mongan, J., Kallianos, K., Khandwala, N., et al. (2026) · Nature Biomedical Engineering · doi:10.1038/s41551-026-01637-3 · Cited by 16
Pinto, I., Olazarán, Á., Jurío, D., de la Osa, B., Sainz, M., Oscoz, A., Ballaz, J., Gorricho, J., Galar, M., & Andonegui, J. (2025) · Frontiers in Digital Health · doi:10.3389/fdgth.2025.1547045 · Cited by 14
Bolocan, V.-O., Mitoi, A., Nicu-Canareica, O., Băean, M.-L., Medar, C., & Popa, G.-A. (2025) · Journal of Imaging · doi:10.3390/jimaging11100335 · Cited by 19
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Zając, H. D., Ribeiro, J., Ingala, S., Gentile, S., Wanjohi, R., Gitau, S. N., Carlsen, J. F., Nielsen, M. B., & Andersen, T. O. (2024) · Designing Interactive Systems Conference · doi:10.1145/3643834.3660707 · Cited by 17
Tagi, M., Hamada, Y., Xiao, S., Ozaki, K., Kubota, M., Amano, S., Sakaue, H., Suzuki, Y., Konishi, T., & Hirose, J. (2024) · JMIR Formative Research · doi:10.2196/55218 · Cited by 14
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Liu, T. Y. A., & Bressler, N. M. (2020) · Current Opinion in Ophthalmology · doi:10.1097/icu.0000000000000694 · Cited by 15
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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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