Linguistically Communicating Uncertainty in Patient-Facing Risk Prediction Models
This addresses the problem of patient understanding in healthcare AI, but it is incremental as it focuses on a specific application without broad validation.
The paper tackles the challenge of communicating uncertainty in AI risk prediction models to patients, proposing a design for natural language communication in the context of in-vitro fertilization outcome prediction.
This paper addresses the unique challenges associated with uncertainty quantification in AI models when applied to patient-facing contexts within healthcare. Unlike traditional eXplainable Artificial Intelligence (XAI) methods tailored for model developers or domain experts, additional considerations of communicating in natural language, its presentation and evaluating understandability are necessary. We identify the challenges in communication model performance, confidence, reasoning and unknown knowns using natural language in the context of risk prediction. We propose a design aimed at addressing these challenges, focusing on the specific application of in-vitro fertilisation outcome prediction.