Linguistically Communicating Uncertainty in Patient-Facing Risk Prediction Models

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 consideration...

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Veröffentlicht in:arXiv.org 2024-01
Hauptverfasser: Sivaprasad, Adarsa, Reiter, Ehud
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Sprache:eng
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Explainable artificial intelligence
In vitro fertilization
Prediction models
Risk communication
Uncertainty
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description 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.
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subjects Explainable artificial intelligence
In vitro fertilization
Prediction models
Risk communication
Uncertainty
title Linguistically Communicating Uncertainty in Patient-Facing Risk Prediction Models
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