Low AI confidence reduced clinician trust and agreement and increased decision time, while high confidence corresponded to a small drop in diagnostic accuracy in a 28-participant web experiment.
The Impact of AI Explanations on Clinicians Trust and Diagnostic Accuracy in Breast Cancer
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
abstract
Advances in machine learning have created new opportunities to develop artificial intelligence (AI)-based clinical decision support systems using past clinical data and improve diagnosis decisions in life-threatening illnesses such breast cancer. Providing explanations for AI recommendations is a possible way to address trust and usability issues in black-box AI systems. This paper presents the results of an experiment to assess the impact of varying levels of AI explanations on clinicians' trust and diagnosis accuracy in a breast cancer application and the impact of demographics on the findings. The study includes 28 clinicians with varying medical roles related to breast cancer diagnosis. The results show that increasing levels of explanations do not always improve trust or diagnosis performance. The results also show that while some of the self-reported measures such as AI familiarity depend on gender, age and experience, the behavioral assessments of trust and performance are independent of those variables.
citation-role summary
background 1
citation-polarity summary
fields
cs.HC 1years
2025 1verdicts
REJECT 1roles
background 1polarities
background 1representative citing papers
citing papers explorer
-
Explainability and AI Confidence in Clinical Decision Support Systems: Effects on Trust, Diagnostic Performance, and Cognitive Load in Breast Cancer Care
Low AI confidence reduced clinician trust and agreement and increased decision time, while high confidence corresponded to a small drop in diagnostic accuracy in a 28-participant web experiment.