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Understanding the impact of explanations on advice-taking: a user study for AI-based clinical Decision Support Systems

Authors:

Cecilia Panigutti,

Andrea Beretta,

Fosca Giannotti,

Dino PedreschiAuthors Info & Claims

CHI '22: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems

Article No.: 568, Pages 1 - 9

https://doi.org/10.1145/3491102.3502104

Published: 29 April 2022 Publication History

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Abstract

The field of eXplainable Artificial Intelligence (XAI) focuses on providing explanations for AI systems’ decisions. XAI applications to AI-based Clinical Decision Support Systems (DSS) should increase trust in the DSS by allowing clinicians to investigate the reasons behind its suggestions. In this paper, we present the results of a user study on the impact of advice from a clinical DSS on healthcare providers’ judgment in two different cases: the case where the clinical DSS explains its suggestion and the case it does not. We examined the weight of advice, the behavioral intention to use the system, and the perceptions with quantitative and qualitative measures. Our results indicate a more significant impact of advice when an explanation for the DSS decision is provided. Additionally, through the open-ended questions, we provide some insights on how to improve the explanations in the diagnosis forecasts for healthcare assistants, nurses, and doctors.

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Cited By

Gomez CCho SKe SHuang CUnberath M(2025)Human-AI collaboration is not very collaborative yet: a taxonomy of interaction patterns in AI-assisted decision making from a systematic reviewFrontiers in Computer Science10.3389/fcomp.2024.15210666Online publication date: 6-Jan-2025
https://doi.org/10.3389/fcomp.2024.1521066
Zeng JChen KWang RLi YFan MWu KQi XWang L(2025)ContractMind: Trust-calibration interaction design for AI contract review toolsInternational Journal of Human-Computer Studies10.1016/j.ijhcs.2024.103411196(103411)Online publication date: Feb-2025
https://doi.org/10.1016/j.ijhcs.2024.103411
State LBringas Colmenarejo ABeretta ARuggieri STurini FLaw S(2025)The explanation dialogues: an expert focus study to understand requirements towards explanations within the GDPRArtificial Intelligence and Law10.1007/s10506-024-09430-wOnline publication date: 13-Jan-2025
https://doi.org/10.1007/s10506-024-09430-w
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Index Terms

Understanding the impact of explanations on advice-taking: a user study for AI-based clinical Decision Support Systems
1. Computing methodologies
  1. Artificial intelligence
    1. Philosophical/theoretical foundations of artificial intelligence
      1. Cognitive science
2. Human-centered computing
  1. Human computer interaction (HCI)
    1. HCI design and evaluation methods
      1. User studies

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cover image ACM Conferences

CHI '22: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems

April 2022

10459 pages

ISBN:9781450391573

DOI:10.1145/3491102

Editors:
Simone Barbosa
PUC-Rio, Brazil
,
Cliff Lampe
University of Michigan, USA
,
Caroline Appert
Université Paris-Saclay, France
,
David A. Shamma
Toyota Research Institute, USA
,
Steven Drucker
Microsoft Research, USA
,
Julie Williamson
University of Glasgow, UK
,
Koji Yatani
University of Tokyo, Japan

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https://doi.org/10.3389/fcomp.2024.1521066
Zeng JChen KWang RLi YFan MWu KQi XWang L(2025)ContractMind: Trust-calibration interaction design for AI contract review toolsInternational Journal of Human-Computer Studies10.1016/j.ijhcs.2024.103411196(103411)Online publication date: Feb-2025
https://doi.org/10.1016/j.ijhcs.2024.103411
State LBringas Colmenarejo ABeretta ARuggieri STurini FLaw S(2025)The explanation dialogues: an expert focus study to understand requirements towards explanations within the GDPRArtificial Intelligence and Law10.1007/s10506-024-09430-wOnline publication date: 13-Jan-2025
https://doi.org/10.1007/s10506-024-09430-w
Kovari A(2024)AI for Decision Support: Balancing Accuracy, Transparency, and Trust Across SectorsInformation10.3390/info1511072515:11(725)Online publication date: 11-Nov-2024
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