Decoding the black box: Explainable AI (XAI) for cancer diagnosis, prognosis, and treatment planning-A state-of-the art systematic review.

Journal: International journal of medical informatics
Published Date: Nov 4, 2024

Abstract

OBJECTIVE: Explainable Artificial Intelligence (XAI) is increasingly recognized as a crucial tool in cancer care, with significant potential to enhance diagnosis, prognosis, and treatment planning. However, the holistic integration of XAI across all stages of cancer care remains underexplored. This review addresses this gap by systematically evaluating the role of XAI in these critical areas, identifying key challenges and emerging trends.

Authors

  • Yusuf Abas Mohamed
    School of Electrical & Electronic Engineering, Engineering Campus, Universiti Sains Malaysia (USM), Malaysia.
  • Bee Ee Khoo
    School of Electrical & Electronic Engineering, Engineering Campus, Universiti Sains Malaysia (USM), Malaysia. Electronic address: beekhoo@eng.usm.my.
  • Mohd Shahrimie Mohd Asaari
    School of Electrical & Electronic Engineering, Engineering Campus, Universiti Sains Malaysia (USM), Malaysia.
  • Mohd Ezane Aziz
    Department of Radiology, School of Medical Sciences, Health Campus, Universiti Sains Malaysia (USM), Kelantan, Malaysia.
  • Fattah Rahiman Ghazali
    Department of Radiology, School of Medical Sciences, Health Campus, Universiti Sains Malaysia (USM), Kelantan, Malaysia.

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