Appealing but Potentially Biasing - Investigation of the Visual Representation of Segmentation Predictions by AI Recommender Systems for Medical Decision Making-Reference-Cited by-同舟云学术

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Appealing but Potentially Biasing - Investigation of the Visual Representation of Segmentation Predictions by AI Recommender Systems for Medical Decision Making

Published:2023-09-03 Issue: Volume: Page:
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Container-title:Mensch und Computer 2023
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Author:

Ammeling Jonas¹^ORCID,Manger Carina¹^ORCID,Kwaka Elias¹^ORCID,Krügel Sebastian¹^ORCID,Uhl Matthias¹^ORCID,Kießig Angelika²^ORCID,Fritz Alexis²^ORCID,Ganz Jonathan¹^ORCID,Riener Andreas¹^ORCID,Bertram Christof A.³^ORCID,Breininger Katharina⁴^ORCID,Aubreville Marc¹^ORCID

Affiliation:

1. Technische Hochschule Ingolstadt, Deutschland

2. Katholische Universität Eichstätt, Deutschland

3. University of Veterinary Medicine Vienna, Österreich

4. Friedrich-Alexander-Universität Erlangen-Nürnberg, Deutschland

Publisher

ACM

Reference32 articles.

1. “If I Had All the Time in the World”: Ophthalmologists’ Perceptions of Anchoring Bias Mitigation in Clinical AI Support

2. Does the Whole Exceed its Parts? The Effect of AI Explanations on Complementary Team Performance

3. Human-Centered Tools for Coping with Imperfect Algorithms During Medical Decision-Making

4. A Case for Humans-in-the-Loop: Decisions in the Presence of Erroneous Algorithmic Scores

5. A Task-Based Taxonomy of Cognitive Biases for Information Visualization

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