Human-Centered Tools for Coping with Imperfect Algorithms During Medical Decision-Making
Open accessAbstract
Machine learning (ML) is increasingly being used in image retrieval systems for medical decision making. One application of ML is to retrieve visually similar medical images from past patients (e.g. tissue from biopsies) to reference when making a medical decision with a new patient. However, no algorithm can perfectly capture an expert's ideal notion of similarity for every case: an image that is algorithmically determined to be similar may not be medically relevant to a doctor's specific diagnostic needs. In this paper, we identified the needs of pathologists when searching for similar images retrieved using a deep learning algorithm, and developed tools that empower users to cope with the search algorithm on-the-fly, communicating what types of similarity are most important at different moments in time. In two evaluations with pathologists, we found that these tools increased the diagnostic utility of images found and increased user trust in the algorithm. The tools were preferred over a traditional interface, without a loss in diagnostic accuracy. We also observed that users adopted new strategies when using refinement tools, re-purposing them to test and understand the underlying algorithm and to disambiguate ML errors from their own errors. Taken together, these findings inform future human-ML collaborative systems for expert decision-making.
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Humans can review AI explanations to understand how AI models generate outputs and to identify whether an AI output is right or not.
“In particular, humans can review AI explanations to understand how the AI models generate outputs [61] and identify whether an AI output is right or not [14, 42].”
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 [14]
The cited paper supports the [61] portion about users gaining insight into an algorithm: ‘Developing a Mental Model of the ML’ describes pathologists testing theories about surprising results. But ‘Disambiguating ML Errors from Self Errors’ says they did so without an explicit explanation, using refinement tools rather than reviewing AI explanations. The separate claim about judging whether an output is right is attached to [14, 42], not this source. (LLM, conf=0.86)
Experimental — accuracy not yet measuredPrevious studies of deployed systems have used post-hoc surveys or interviews to gauge provider impressions.
“In order to better understand factors impacting the adoption of CDS, previous studies of deployed systems have used post-hoc surveys or interviews to gauge provider impressions 28,44,53 ; however, provider impressions can differ from their actual use 4,18,27 .”
From Factors driving provider adoption of the TREWS machine learning-based early warning system and its effects on sepsis treatment timing (2022) · cites this paper as [28]
The Method section reports that pathologists completed a post-study questionnaire and semi-structured interview, and User Study Results reports their impressions of SMILY. But the Method describes a controlled study using six images, not a study of a deployed clinical system; that qualifier is unsupported. (LLM, conf=0.86)
Experimental — accuracy not yet measuredEase of use and integration into the workflow have been noted as important factors influencing adoption.
“Additionally, ease of use and integration into the workflow have been noted as important factors influencing adoption 26,28,47,48 .”
From Factors driving provider adoption of the TREWS machine learning-based early warning system and its effects on sepsis treatment timing (2022) · cites this paper as [28]
The User Study Results report that SMILY required less effort than a conventional interface and that participants were more likely to use it in clinical practice. This supports a connection between usability and prospective use, but the paper does not identify integration into clinical workflow as an adoption factor. (LLM, conf=0.83)
Experimental — accuracy not yet measuredExplainability of a system is critical for human-AI collaborative decision-making.
“Many researchers have discussed that the explainability [61] of a system is critical for humanAI collaborative decision-making [14, 42].”
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 [14]
Several mechanisms have been proposed to optimize the combination of human and AI decisions and facilitate appropriate reliance, including interpretable explanations, cognitive forcing functions, and feedback loops.
“These mechanisms include providing interpretable explanations for AI advice [21, 72, 123], using cognitive forcing functions[17, 47, 99], and incorporating feedback loops to enhance the interaction between humans and AI systems [10, 11, 139].”
From Dealing with Uncertainty: Understanding the Impact of Prognostic Versus Diagnostic Tasks on Trust and Reliance in Human-AI Decision Making (2024) · cites this paper as [21]
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A Human-AI Collaborative Approach for Clinical Decision Making on Rehabilitation Assessment
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BreastScreening-AI: Evaluating medical intelligent agents for human-AI interactions
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A Review of Recent Deep Learning Approaches in Human-Centered Machine Learning
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"Why is 'Chicago' deceptive?" Towards Building Model-Driven Tutorials for Humans
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Mental Models of AI Agents in a Cooperative Game Setting
Gero, K. I., Ashktorab, Z., Dugan, C., Pan, Q., Johnson, J., Geyer, W., Ruiz, M., Miller, S., Millen, D. R., Campbell, M., et al. (2020) · Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3313831.3376316 · Cited by 121 · see citing works
A Review on Human–AI Interaction in Machine Learning and Insights for Medical Applications
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Explainable Convolutional Neural Networks: A Taxonomy, Review, and Future Directions
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PromptCharm: Text-to-Image Generation through Multi-modal Prompting and Refinement
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Research Trends in Artificial Intelligence Applications in Human Factors Health Care: Mapping Review
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Modeling adoption of intelligent agents in medical imaging
Calisto, F. M., Nunes, N., & Nascimento, J. C. (2022) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2022.102922 · Cited by 111 · see citing works
Who Goes First? Influences of Human-AI Workflow on Decision Making in Clinical Imaging
Fogliato, R., Chappidi, S., Lungren, M. P., Fisher, P., Wilson, D., Fitzke, M., Parkinson, M., Horvitz, E., Inkpen, K. M., & Nushi, B. (2022) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3531146.3533193 · Cited by 115 · see citing works
Acquisition of chess knowledge in AlphaZero
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Assertiveness-based Agent Communication for a Personalized Medicine on Medical Imaging Diagnosis
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Concept attribution: explaining cnn decisions to physicians
M., G., V., A., S., M.-M., & H., M. (2020) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2020.103865 · Cited by 95 · see citing works
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Rajashekar, N., Shin, Y. E., Pu, Y., Chung, S., You, K., Giuffrè, M., Chan, C., Saarinen, T., Hsiao, A., Sekhon, J. S., et al. (2024) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642024 · Cited by 96 · see citing works
Enhancing trust in AI through industry self-governance.
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Human-centred artificial intelligence: a contextual morality perspective
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Wang, A. Y., Wang, D., Drozdal, J., Muller, M. J., Park, S., Weisz, J. D., Liu, X., Wu, L., & Dugan, C. (2022) · ACM Transactions on Computer-Human Interaction · doi:10.1145/3489465 · Cited by 82 · see citing works
Yildirim, N., Richardson, H., Wetscherek, M. T., Bajwa, J., Jacob, J., Pinnock, M. A., Harris, S., Coelho De Castro, D., Bannur, S., Hyland, S., et al. (2024) · Proceedings of the CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642013 · Cited by 82 · see citing works
Human-AI Collaboration for UX Evaluation: Effects of Explanation and Synchronization
Fan, M., Yang, X., Yu, T., Liao, Q. V., & Zhao, J. (2022) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3512943 · Cited by 81 · see citing works
XAIR: A Framework of Explainable AI in Augmented Reality
Xu, X., Yu, A., Jonker, T. R., Todi, K., Lu, F., Qian, X., Belo, J. M. E., Wang, T., Li, M., Mun, A., et al. (2023) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3544548.3581500 · Cited by 84 · see citing works
A Survey of Human‐Centered Evaluations in Human‐Centered Machine Learning
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Lee, M. H., & Chew, C. J. (2023) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3610218 · Cited by 78 · see citing works
Identifying Challenges and Opportunities in Human-AI Collaboration in Healthcare
Park, S. Y., Kuo, P.-Y. P., Barbarin, A. M., Kaziunas, E., Chow, A., Singh, K., Wilcox, L. G., & Lasecki, W. S. (2019) · ACM Conference on Computer Supported Cooperative Work (CSCW) · doi:10.1145/3311957.3359433 · Cited by 74 · see citing works
Zając, H. D., Li, D., Dai, X., Carlsen, J. F., Kensing, F., & Andersen, T. O. (2023) · ACM Transactions on Computer-Human Interaction · doi:10.1145/3582430 · Cited by 76 · see citing works
Debugging Tests for Model Explanations
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Benk, M., Kerstan, S., von Wangenheim, F., & Ferrario, A. (2024) · AI & Society · doi:10.1007/s00146-024-02059-y · Cited by 78 · see citing works
Toward involving end-users in interactive human-in-the-loop AI fairness
Nakao, Y., Stumpf, S., Ahmed, S., Naseer, A., & Strappelli, L. (2022) · ACM Transactions on Interactive Intelligent Systems · doi:10.1145/3514258 · Cited by 69 · see citing works
The TA Framework: Designing Real-time Teaching Augmentation for K-12 Classrooms
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X-MIR: EXplainable Medical Image Retrieval
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Towards relatable explainable AI with the perceptual process
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Holding AI to Account: Challenges for the Delivery of Trustworthy AI in Healthcare
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AI Knowledge: Improving AI Delegation through Human Enablement
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Facilitating Knowledge Sharing from Domain Experts to Data Scientists for Building NLP Models
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Lessons Learned from Designing an AI-Enabled Diagnosis Tool for Pathologists
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Artificial intelligence in glioma imaging: Challenges and advances
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Jesso, S. T., Kelliher, A., Sanghavi, H. K., Martin, T., & Parker, S. H. (2022) · Frontiers in Psychology · doi:10.3389/fpsyg.2022.830345 · Cited by 59 · see citing works
Human-in-the-loop Extraction of Interpretable Concepts in Deep Learning Models
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Selective Explanations: Leveraging Human Input to Align Explainable AI
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Survey of XAI in Digital Pathology
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Explanatory pragmatism: a context-sensitive framework for explainable medical AI
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Mitigating knowledge imbalance in AI-advised decision-making through collaborative user involvement
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Increasing the Speed and Accuracy of Data Labeling Through an AI Assisted Interface
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Social Sensemaking with AI: Designing an Open-ended AI Experience with a Blind Child
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Designing for Interactive Exploratory Data Analysis Requires Theories of Graphical Inference
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Influences on User Trust in Healthcare Artificial Intelligence: A Systematic Review
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A Missing Piece in the Puzzle: Considering the Role of Task Complexity in Human-AI Decision Making
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- Authors
- Cai, C. J., Reif, E., Hegde, N., Hipp, J., Kim, B., Smilkov, D., Wattenberg, M., Viegas, F., Corrado, G. S., Stumpe, M. C., & Terry, M.
- Affiliations
- Google (United States) (US)
- Google Brain, Mountain View, CA, USA
- ORCID
- Venue
- Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems
- Publisher
- ACM
- Published
- 2019-05-02
- Pages
- 1-14
- OpenAlex
- W2918341242
- Cited by
- 405
- References
- 50
- Citations by year
- 2026: 27 · 2025: 51 · 2024: 80 · 2023: 73 · 2022: 73 · 2021: 54 · 2020: 38 · 2019: 9
- FWCI
- 37.29
- Type
- conference paper
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- CHI Conference on Human Factors in Computing Systems (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Physical Sciences › Computer Science › Information Systems › Information Retrieval and Search Behavior
- Keywords
- medical image retrieval, pathologists, human-machine collaboration, trust in machine learning, diagnostic accuracy, human-centered design
About these checks
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.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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