The DOI matches its Crossref record; 51 of the 51 references it cites were found in a registry or an index.
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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“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]
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“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]
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“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]
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“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]
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“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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Human-computer collaboration for skin cancer recognition
Tschandl, P., Rinner, C., Apalla, Z., Argenziano, G., Codella, N., Halpern, A., Janda, M., Lallas, A., Longo, C., Malvehy, J., et al. (2020) · Nature Medicine · doi:10.1038/s41591-020-0942-0 · Cited by 1,024
Beede, E., Baylor, E., Hersch, F., Iurchenko, A., Wilcox, L., Ruamviboonsuk, P., & Vardoulakis, L. M. (2020) · Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3313831.3376718 · Cited by 614
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-citationA Multidisciplinary Survey and Framework for Design and Evaluation of Explainable AI Systems
Mohseni, S., Zarei, N., & Ragan, E. D. (2021) · ACM Transactions on Interactive Intelligent Systems · doi:10.1145/3387166 · Cited by 785
Six Human-Centered Artificial Intelligence Grand Challenges
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Metadata
- 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
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