Explainable Artificial Intelligence (XAI) Approaches in Healthcare Diagnostics and Analysis
Year: 2025
Type: article
Abstract: Explainable Artificial Intelligence (XAI) has emerged as a critical field for enabling trustworthy, transparent, and clinically acceptable AI-driven diagnostic systems. While deep learning and complex ensemble models have achieved high performance on many diagnostic tasks (image interpretation, disease risk prediction, time-series monitoring), their “black-box” nature creates barriers for clinical adoption due to safety, accountability, and regulatory requirements. This paper reviews major XAI approaches used in healthcare diagnostics, presents a taxonomy of methods (intrinsically interpretable models, post-hoc explanation techniques, model-agnostic vs. model-specific approaches), surveys typical applications and case studies, discusses datasets and evaluation metrics for explanations, outlines methodological and practical challenges (stability, fidelity, evaluation, human factors), and highlights ethical, legal, and regulatory considerations. We conclude with future research directions emphasizing hybrid human–AI workflows, standardized evaluation frameworks, and techniques to reconcile explain ability with high performance in safety-critical clinical contexts.
Source: Journal of Carcinogenesis
Authors: Dr. Sandeep Mishra, Ms. P Swati, Ms. Manka Sharma, Mr. Debabrata Maity, Mr. Suman Kumar Bhattacharyya, Dr. P. Loganathan
Institution: Galgotias University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,500
OpenAlex ID: W7083317476
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