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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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Improving the Efficiency of Human-in-the-Loop Systems: Adding Artificial to Human Experts
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Learning to predict with supporting evidence
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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
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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