Designing for Physician Trust: Toward a Machine Learning Decision Aid for Radiation Toxicity Risk

The application of machine learning (ML) technologies in health care is expected to improve care delivery and patient outcomes. However, there are no best practices for designing these technologies for use in clinical settings. To explore user needs and design requirements for a user interface of a ML risk prediction tool in development, we consulted with subject matter experts and physicians. We explored physician expectations of using a ML tool in clinical practice and their preferences on designs. Our process revealed physician perspectives on trusting a ML tool and opportunities to design for these considerations, while navigating ambiguity in the tool’s outputs.