Summary of Linguistically Communicating Uncertainty in Patient-facing Risk Prediction Models, by Adarsa Sivaprasad and Ehud Reiter
Linguistically Communicating Uncertainty in Patient-Facing Risk Prediction Models
by Adarsa Sivaprasad, Ehud Reiter
First submitted to arxiv on: 31 Jan 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this paper, researchers tackle the problem of uncertainty quantification in AI models used in patient-facing healthcare contexts. Unlike traditional explainable AI (XAI) methods designed for developers or domain experts, this work focuses on communicating with patients and evaluating understandability. The authors identify challenges in presenting model performance, confidence, and reasoning using natural language, particularly when predicting risk outcomes. To address these challenges, the researchers propose a design specifically tailored to in-vitro fertilisation (IVF) outcome prediction. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps make artificial intelligence work better in healthcare. Right now, AI models are used to help doctors make decisions, but they don’t always tell us how sure they are about their answers. When we need to explain this to patients, it gets even harder. The researchers behind this project wanted to find a way to fix these issues. They looked at the problem of predicting IVF success and found that current AI models aren’t good enough. So, they came up with a new design to help us better understand AI’s predictions. |
