Evidence-based XAI of clinical decision support systems for differential diagnosis: Design, implementation, and evaluation

Abstract

Introduction: We propose the Explainable AI (XAI) model for Clinical Decision Support Systems (CDSSs). It supports physician's Differential Diagnosis (DDx) with Evidence-based Medicine (EBM). It identifies instances of the case data contributing to predicted diseases. Each case data is linked to the sourced medical literature. Therefore, this model can provide medical professionals with evidence of predicted diseases. Methods: The source of the case data (training data) is medical literature. The prediction model (the main model) uses Neural Network (NN) + Learning To Rank (LTR). Physicians' DDx and machines' LTR are remarkably similar. The XAI model (the surrogate model) uses k-Nearest Neighbors Surrogate model (k-NN Surrogate model). The k-NN Surrogate model is a symphony of Example-based explanations, Local surrogate model, and k-Nearest Neighbors (k-NN). Requirements of the XAI for CDSS and features of the XAI model are remarkably adaptable. To improve the surrogate model's performance, it performs "Selecting its data closest to the main model." We evaluated the prediction and XAI performance of the models. Results: With the effect of "Selecting," the surrogate model's prediction and XAI performances are higher than those of the "standalone" surrogate model. Conclusions: The k-NN Surrogate model is a useful XAI model for CDSS. For CDSSs with similar aims and features, the k-NN Surrogate model is helpful and easy to implement. The k-NN Surrogate model is an Evidence-based XAI for CDSSs. Unlike current commercial Large Language Models (LLMs), Our CDSS shows evidence of predicted diseases to medical professionals.