Moving Toward Explainable Decisions of Artificial Intelligence Models for the Prediction of Functional Outcomes of Ischemic Stroke Patients

Abstract

Artificial intelligence has the potential to assist clinical decision-making for the treatment of ischemic stroke. However, the decision processes encoded within complex artificial intelligence models, such as neural networks, are notoriously difficult to interpret and validate. The importance of explaining model decisions has resulted in the emergence of explainable artificial intelligence, which aims to understand the inner workings of artificial intelligence models. Here, we give examples of studies that apply artificial intelligence models to predict functional outcomes of ischemic stroke patients, evaluate existing models’ predictive power, and discuss the challenges that limit their adaptation to the clinic. Furthermore, we identify the studies that explain which model features are essential in predicting functional outcomes. We discuss how these explanations can help mitigate concerns around the trustworthiness of artificial intelligence systems developed for the acute stroke setting. We conclude that explainable artificial intelligence is a must for the reliable deployment of artificial intelligence models in acute stroke care.

Ähnliche Arbeiten