Towards a Transparent and Interpretable AI Model for Medical Image Classifications
It addresses the challenge of AI interpretability for clinical applications, but is incremental as it surveys and simulates existing XAI methods without introducing new techniques.
This paper tackles the problem of opacity in AI models for medical image classification by investigating explainable AI (XAI) methods to make decisions transparent and interpretable, with simulations on medical datasets showing how XAI improves decision-making for healthcare professionals.
The integration of artificial intelligence (AI) into medicine is remarkable, offering advanced diagnostic and therapeutic possibilities. However, the inherent opacity of complex AI models presents significant challenges to their clinical practicality. This paper focuses primarily on investigating the application of explainable artificial intelligence (XAI) methods, with the aim of making AI decisions transparent and interpretable. Our research focuses on implementing simulations using various medical datasets to elucidate the internal workings of the XAI model. These dataset-driven simulations demonstrate how XAI effectively interprets AI predictions, thus improving the decision-making process for healthcare professionals. In addition to a survey of the main XAI methods and simulations, ongoing challenges in the XAI field are discussed. The study highlights the need for the continuous development and exploration of XAI, particularly from the perspective of diverse medical datasets, to promote its adoption and effectiveness in the healthcare domain.