The Impact of AI Explanations on Clinicians Trust and Diagnostic Accuracy in Breast Cancer

Understand

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.