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All Journal PERFECT: Journal of Smart Algorithms
Al-Rekabi, Mohammed
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Library & Information Science

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Explainable AI for Medical Imaging: A Taxonomy Based on Clinical Task Requirements Kamber, Ali Nadhim; Alkaabi, Hussein; Al-Rekabi, Mohammed; Jasim, Ali Kadhim
PERFECT: Journal of Smart Algorithms Vol. 2 No. 2 (2025): PERFECT: Journal of Smart Algorithms, Article Research July 2025
Publisher : LEMBAGA KAJIAN PEMBANGUNAN PERTANIAN DAN LINGKUNGAN (LKPPL)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62671/perfect.v2i2.115

Abstract

Explainable Artificial Intelligence (XAI) has emerged as a critical enabler for deploying AI-driven medical imaging systems where transparency, trust, and accountability are paramount. However, most current taxonomies of XAI methods categorize techniques based on algorithmic families (e.g., saliency maps, attribution methods), which often fail to reflect the practical requirements of clinical tasks. This paper proposes a novel task-centric taxonomy of XAI in medical imaging that aligns explanation techniques with four key clinical tasks: classification, detection, segmentation, and prognostic assessment. For each task, we analyze how different XAI methods enhance model interpretability, their suitability for clinical decision-making, and their limitations in real-world applications. Our taxonomy aims to provide a practical framework for researchers and practitioners to select appropriate XAI strategies tailored to the specific demands of medical imaging workflows. Furthermore, we highlight the current gaps in task-specific explainability and propose future research directions towards clinically meaningful, task-driven XAI solutions. This work serves as a step towards bridging the gap between technical XAI developments and the functional needs of clinical practice.
Co-Authors Alkaabi, Hussein Jasim, Ali Kadhim Kamber, Ali Nadhim