Exploring Explainable Artificial Intelligence (XAI) in Diagnosing and Treating Tumours of the Female Reproductive System

Abstract

Female reproductive cancers including ovarian cancer together with cervical and uterine cancers present multiple diagnostic hurdles while requiring advanced therapeutic solutions. The patient's chances for better treatment outcomes depend heavily on early-stage detection together with treatment planning accuracy yet current diagnostic tools display poor precision levels. This analysis evaluates the implementation of interpretable artificial intelligence (XAI) systems for recognizing and treating reproductive system tumours. The lack of transparency in "black-box" traditional models prevents widespread clinical adoption of artificial intelligence (AI) which shows vast healthcare improvements potential. A design feature built into interpretable AI functions enables clinicians to both trust and understand the decisions generated by AI systems. This research looks at contemporary AI diagnostic applications for tumours as well as the utilization of interpretive XAI approaches SHAP and LIME and the adoption of AI in therapeutic agenda development. This paper explores both clinical adoption obstacles and shows the innovative solutions that address these barriers. The findings demonstrate how XAI functions to enhance tumour detection precision and tailor treatment approaches and improve clinical decision-making.