Human-Centered Tools for Coping with Imperfect Algorithms during Medical Decision-Making
| Autor: | Fernanda B. Viégas, Narayan Hegde, Jason D. Hipp, Martin Wattenberg, Emily Reif, Been Kim, Greg S. Corrado, Michael Terry, Martin C. Stumpe, Daniel Smilkov, Carrie J. Cai |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
FOS: Computer and information sciences
Coping (psychology) Computer science business.industry Deep learning 05 social sciences Computer Science - Human-Computer Interaction 020207 software engineering 02 engineering and technology Medical decision making Human-Computer Interaction (cs.HC) Computer Science - Computers and Society Search algorithm Computers and Society (cs.CY) 0202 electrical engineering electronic engineering information engineering 0501 psychology and cognitive sciences Imperfect Artificial intelligence business Image retrieval Algorithm 050107 human factors |
| Zdroj: | CHI |
| DOI: | 10.48550/arxiv.1902.02960 |
| Popis: | 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 refinement 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|