[2A1-GS-10] AI application: Medicine / Healthcare

[2A1-GS-10-04] Can AI Clinical Decision Support System show Evidence and Humility?

Fusion of XAI and UQ with Surrogate model

〇Yasuhiko Miyachi², Osamu Ishii², Keijiro Torigoe^1,2 (1. Torigoe Clinic, 2. The Society for Computer-aided Clinical Decision Support System)

Keywords:Clinical Decision Support System, Explainable Artificial Intelligence, Example-based Explanation, Uncertainty Quantification, Conformal Prediction

Objectives: We propose the XAI and UQ (Uncertainty Quantification) for the Clinical Decision Support System (CDSS). The based CDSS was presented at JSAI2022.
Method: The XAI and UQ use the "same" surrogate model (k-NN Surrogate model) based on the k-Nearest Neighbors. The XAI method is an Example-based Explanation. This model outputs information about the medical literature and diseases from instances of training data. The UQ method is Conformal Prediction. The Difficulty Estimator of this model outputs Difficulty scores. By "Processing to closest" of the surrogate model, the predicted data of the surrogate model are close to that of the main model.
Conclusions: Our proposed XAI and UQ could be adapted for other CDSSs. Unlike current commercial LLMs, prediction, XAI, and UQ of our CDSS can provide evidence and uncertainty information to medical professionals.