Paper 6: Explainable AI (XAI) Techniques to Enhance Cancer Diagnosis
Authors : Harmeet Khera, Nikhil Pandey and Dr. Shalini Lamb
Doi: https://doi.org/10.63920/tjths.43006
Abstract
This study examines how Explainable Artificial Intelligence (XAI) enhances diagnosis of cancer, emphasizing the importance of trust and transparency in the clinical sector. While artificial intelligence (AI) models have demonstrated impressive accuracy in detecting cancer from medical images, their black-box nature often prevents doctors from understanding how predictions are made. This lack of interpretability creates hesitation in adopting AI for real-world healthcare. The paper examines popular XAI methods which provide visual and feature-based explanations of model outputs. It also discusses how these methods can enhance clinician confidence, reduce diagnostic errors, and meet regulatory requirements for accountability. This research emphasizes the promise of interpretable models in connecting the precision of machine learning with the trustworthiness of medical practices, based on a review of recent studies and a suggested framework for applying XAI in oncology. Additionally, it discusses future strategies for incorporating XAI into clinical processes.
