From Experience to Experience: Key Insights for Improved Interaction with AI in Radiology

This is the authors' abstract. We don't add key points for this paper.

Artificial Intelligence (AI) decision-making tools for radiology demonstrated potential capacity to improve radiology work in several tasks such as tumor detection. However, relatively low acceptance in clinical practice demonstrates the challenge of incorporating end-users' lived experience and their opinions to improve the interaction between clinicians and AI solutions. Therefore, we conducted semi-structured interviews with radiologists and technicians who had lived experience with current or the prior generations of radiology AI tools (e.g., Computer Aided Decision tools). Three key themes were elicited. Firstly, the role of AI, addresses how radiology professionals interact with radiology AI; the second theme, adoption in practice, discusses the requirements for easy usage and smooth transition; the third theme, building appropriate trust, explores influencing factors of clinicians' trust towards radiology AI. Our findings call attention to the adoption of actionable recommendations on the interaction design and the importance of individual tailored functionalities in radiology AI systems.