Resolvable Artificial Intelligence Method in Smart Healthcare

Abstract

Autonomous vehicles, social networking and online store recommender systems, financial technology, question answering systems, and natural language processing are just a few areas that have made extensive use of Machine Learning (ML) algorithms. Since its inception fifty years ago, the rule-based approach to illness diagnosis and clinical decision support has garnered considerable attention. This strategy is focused on curating medical information and building powerful decision rules. Predictive modeling in healthcare has recently shown promise for ML algorithms that can account for complicated connections between features. Despite the impressive effectiveness of many ML algorithms, their lack of explainability makes them difficult to fully use in real-world clinical settings. Explainable artificial intelligence (XAI) is developing to help people explain their inner thoughts, feelings, and behaviors to medical experts. The ability to understand how to use predictive modeling in real-life situations somewhat objective carelessly resulting the predictions is a key component of XAI's success in gaining physicians' confidence. Because medical knowledge is complicated, there are still numerous possibilities to investigate in order to bring XAI to a clinical context where it may be effective.