The Effect of Explainable AI and Uncertainty Quantification on Medical Students’ Perspectives of Decision-Making AI: A Cancer Screening Case Study

Sing Yee Toh; Chang Cai; Li Rong Wang; Xiaoyin Bai; Joanne Ngeow; Xiuyi Fan

doi:10.1145/3706599.3719791

The Effect of Explainable AI and Uncertainty Quantification on Medical Students’ Perspectives of Decision-Making AI: A Cancer Screening Case Study

Sing Yee Toh, Chang Cai, Li Rong Wang, Xiaoyin Bai, Joanne Ngeow, Xiuyi Fan^*

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

Artificial intelligence (AI) offers potential to enhance healthcare decision-making but is limited by its’black box’ nature. Explainable AI (XAI) and Uncertainty Quantification (UQ) address these challenges by improving interpretability and reliability. Despite their potential, the impact of XAI and UQ on medical students’ perception of AI in healthcare remains unclear. This study explores the impact of XAI and UQ on medical students’ perceptions of AI in healthcare. A mixed-method study with 131 medical students from Singapore and China assessed the effects of varying AI methods on trust, usability, and decision-making. Results show that XAI and UQ enhance AI usability but highlight the need for clinically relevant explanations and contextualised uncertainty reasoning to optimise AI adoption in healthcare.

Original language	English
Title of host publication	CHI EA 2025 - Extended Abstracts of the 2025 CHI Conference on Human Factors in Computing Systems
Publisher	Association for Computing Machinery
ISBN (Electronic)	9798400713958
DOIs	https://doi.org/10.1145/3706599.3719791
Publication status	Published - Apr 26 2025
Externally published	Yes
Event	2025 CHI Conference on Human Factors in Computing Systems, CHI EA 2025 - Yokohama, Japan Duration: Apr 26 2025 → May 1 2025

Publication series

Name	Conference on Human Factors in Computing Systems - Proceedings

Conference

Conference	2025 CHI Conference on Human Factors in Computing Systems, CHI EA 2025
Country/Territory	Japan
City	Yokohama
Period	4/26/25 → 5/1/25

Bibliographical note

Publisher Copyright:
© 2025 Copyright held by the owner/author(s).

ASJC Scopus Subject Areas

Human-Computer Interaction
Computer Graphics and Computer-Aided Design
Software

Keywords

Explainable AI
Human-AI Interaction
Medical AI
Uncertainty Quantification

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1145/3706599.3719791

Cite this

Toh, S. Y., Cai, C., Wang, L. R., Bai, X., Ngeow, J., & Fan, X. (2025). The Effect of Explainable AI and Uncertainty Quantification on Medical Students’ Perspectives of Decision-Making AI: A Cancer Screening Case Study. In CHI EA 2025 - Extended Abstracts of the 2025 CHI Conference on Human Factors in Computing Systems Article 515 (Conference on Human Factors in Computing Systems - Proceedings). Association for Computing Machinery. https://doi.org/10.1145/3706599.3719791

Toh, Sing Yee ; Cai, Chang ; Wang, Li Rong et al. / The Effect of Explainable AI and Uncertainty Quantification on Medical Students’ Perspectives of Decision-Making AI : A Cancer Screening Case Study. CHI EA 2025 - Extended Abstracts of the 2025 CHI Conference on Human Factors in Computing Systems. Association for Computing Machinery, 2025. (Conference on Human Factors in Computing Systems - Proceedings).

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abstract = "Artificial intelligence (AI) offers potential to enhance healthcare decision-making but is limited by its{\textquoteright}black box{\textquoteright} nature. Explainable AI (XAI) and Uncertainty Quantification (UQ) address these challenges by improving interpretability and reliability. Despite their potential, the impact of XAI and UQ on medical students{\textquoteright} perception of AI in healthcare remains unclear. This study explores the impact of XAI and UQ on medical students{\textquoteright} perceptions of AI in healthcare. A mixed-method study with 131 medical students from Singapore and China assessed the effects of varying AI methods on trust, usability, and decision-making. Results show that XAI and UQ enhance AI usability but highlight the need for clinically relevant explanations and contextualised uncertainty reasoning to optimise AI adoption in healthcare.",

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month = apr,

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doi = "10.1145/3706599.3719791",

language = "English",

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Toh, SY, Cai, C, Wang, LR, Bai, X, Ngeow, J & Fan, X 2025, The Effect of Explainable AI and Uncertainty Quantification on Medical Students’ Perspectives of Decision-Making AI: A Cancer Screening Case Study. in CHI EA 2025 - Extended Abstracts of the 2025 CHI Conference on Human Factors in Computing Systems., 515, Conference on Human Factors in Computing Systems - Proceedings, Association for Computing Machinery, 2025 CHI Conference on Human Factors in Computing Systems, CHI EA 2025, Yokohama, Japan, 4/26/25. https://doi.org/10.1145/3706599.3719791

The Effect of Explainable AI and Uncertainty Quantification on Medical Students’ Perspectives of Decision-Making AI: A Cancer Screening Case Study. / Toh, Sing Yee; Cai, Chang; Wang, Li Rong et al.
CHI EA 2025 - Extended Abstracts of the 2025 CHI Conference on Human Factors in Computing Systems. Association for Computing Machinery, 2025. 515 (Conference on Human Factors in Computing Systems - Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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AU - Fan, Xiuyi

PY - 2025/4/26

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ER -

Toh SY, Cai C, Wang LR, Bai X, Ngeow J, Fan X. The Effect of Explainable AI and Uncertainty Quantification on Medical Students’ Perspectives of Decision-Making AI: A Cancer Screening Case Study. In CHI EA 2025 - Extended Abstracts of the 2025 CHI Conference on Human Factors in Computing Systems. Association for Computing Machinery. 2025. 515. (Conference on Human Factors in Computing Systems - Proceedings). doi: 10.1145/3706599.3719791

The Effect of Explainable AI and Uncertainty Quantification on Medical Students’ Perspectives of Decision-Making AI: A Cancer Screening Case Study

Abstract

Publication series

Conference

Bibliographical note

ASJC Scopus Subject Areas

Keywords

UN SDGs

Access to Document

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Cite this