Patient-Centred Explainability in IVF Outcome Prediction

TL;DR

This study assesses the understandability of an IVF outcome prediction tool for patients, emphasizing the importance of explainability beyond features and proposing a dialogue-based interface to improve trust and comprehension.

Contribution

It highlights the need for explainability beyond model features in healthcare AI and introduces a dialogue-based interface to enhance user understanding and trust.

Findings

Patients desire explanations beyond feature space

Concerns about data shifts affect trust

Dialogue-based interface improves understanding

Abstract

This paper evaluates the user interface of an in vitro fertility (IVF) outcome prediction tool, focussing on its understandability for patients or potential patients. We analyse four years of anonymous patient feedback, followed by a user survey and interviews to quantify trust and understandability. Results highlight a lay user's need for prediction model \emph{explainability} beyond the model feature space. We identify user concerns about data shifts and model exclusions that impact trust. The results call attention to the shortcomings of current practices in explainable AI research and design and the need for explainability beyond model feature space and epistemic assumptions, particularly in high-stakes healthcare contexts where users gather extensive information and develop complex mental models. To address these challenges, we propose a dialogue-based interface and explore user expectations for personalised explanations.