Transparent AI: Developing an Explainable Interface for Predicting Postoperative Complications

arXiv:2404.16064v1 Announce Type: cross
Abstract: Given the sheer volume of surgical procedures and the significant rate of postoperative fatalities, assessing and managing surgical complications has become a critical public health concern. Existing artificial intelligence (AI) tools for risk surveillance and diagnosis often lack adequate interpretability, fairness, and reproducibility. To address this, we proposed an Explainable AI (XAI) framework designed to answer five critical questions: why, why not, how, what if, and what else, with the goal of enhancing the explainability …

abstract artificial artificial intelligence arxiv become cs.hc cs.lg cs.lo diagnosis fairness health intelligence interpretability public public health rate reproducibility risk surveillance tools transparent transparent ai type