Cancer Detection and Treatment Using Explainable AI

Abstract

Cancer detection and treatment is one of the most difficult tasks in modern medicine, hence it has become a priority for researchers to study and develop specific and targeted procedures to patient outcomes. Just recently, the most promising direction is the humanization of explainable AI (XAI) which is a crucial tool for enhancing AI-based decision making in terms of transparency, and understandability. This paper starts out by describing conventional AI techniques for cancer detection and pointing out the informational gaps then delves into XAI's foundations. It explores the role of XAI in cancer detection and treatment by looking at its potential impact on this topic. It first presents the diagnosis of medical imaging data, then a discussion how patients' data will be interpreted by a XAI system and how treatment strategies that are tailored to the individual patient will be developed by optimizing the therapeutic interventions. This paper also presents comprehensive study of the changing side cancer treatment rendered by XAI and explain why further research and cooperation are getting to be of paramount importance in order for it to be fully exploited. Implementation of XAI in screening and treatment techniques for cancer comes to the conclusion of not only revolutionizing precision medicine, but also aiding in the improvement of patient care and will determine oncology practice in future.