Factors that influence the adoption of human-AI collaboration in clinical decision-making
This addresses the challenge of integrating AI as a coequal partner in healthcare for overworked medical professionals, but it is incremental as it builds on existing research with a human-centered focus.
The study tackled the problem of low adoption of human-AI collaboration in clinical decision-making by identifying key factors through expert interviews, resulting in six adoption factors and highlighting tensions with effective collaboration.
Recent developments in Artificial Intelligence (AI) have fueled the emergence of human-AI collaboration, a setting where AI is a coequal partner. Especially in clinical decision-making, it has the potential to improve treatment quality by assisting overworked medical professionals. Even though research has started to investigate the utilization of AI for clinical decision-making, its potential benefits do not imply its adoption by medical professionals. While several studies have started to analyze adoption criteria from a technical perspective, research providing a human-centered perspective with a focus on AI's potential for becoming a coequal team member in the decision-making process remains limited. Therefore, in this work, we identify factors for the adoption of human-AI collaboration by conducting a series of semi-structured interviews with experts in the healthcare domain. We identify six relevant adoption factors and highlight existing tensions between them and effective human-AI collaboration.