Linguistically Communicating Uncertainty in Patient-Facing Risk Prediction Models

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.