Formulating Reliable Deep Acquisition Hypotheses for Medical Diagnosis Utilizing Explainable AI
Abhay Bhatia1, Rajeev Kumar2, Golnoosh Manteghi3
1Dr. Abhay Bhatia, Post Doctoral Researcher, Department of Computer Science & Engineering, Kuala Lumpur University of Science & Technology (KLUST), Jalan Ikram-Uniten, Kajang, Selangor, Malaysia.
2Prof. (Dr.) Rajeev Kumar, Professor, Department of Computer Science & Engineering, Moradabad Institute of Technology, Moradabad (Uttar Pradesh), India.
3Dr. Golnoosh Manteghi, Faculty of Architecture and Built Environment, Kuala Lumpur University of Science & Technology (KLUST), Jalan Ikram-Uniten, Kajang, Selangor, Malaysia.
Manuscript received on 31 July 2025 | First Revised Manuscript received on 08 August 2025 | Second Revised Manuscript received on 16 September 2025 | Manuscript Accepted on 15 October 2025 | Manuscript published on 30 October 2025 | PP: 1-10 | Volume-5 Issue-6 October 2025 | Retrieval Number:100.1/ijamst.F305005061025 | DOI: 10.54105/ijamst.F3050.05061025
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© The Authors. Published by Lattice Science Publication (LSP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The rise of deep learning has revolutionized various fields, including healthcare. Deep learning models excel at analyzing analyzable medical collections, such as surgical representations and EHRs, offering immense potential for improved medical diagnosis and decision-making. However, a significant barrier to their widespread adoption in clinical practice lies in their inherent “black-box” nature. This deficiency of transparence hinders reliance and raises concerns about accountability in critical medical decisions. This paper explores the concept of Explainable AI (XAI) for medical diagnosis, focusing on building trustworthy deep learning models for clinical decision support. We begin by highlighting the advantages of deep learning in medical diagnosis, emphasizing its ability to identify subtle patterns in data that may elude human experts. We then delve into the limitations of traditional deep learning models, explaining the challenges associated with their opacity and the impact on physician trust. Model-specific methods, on the other hand, leverage the inherent characteristics of specific deep learning architectures to provide insights into their decision-making processes. We then explore the integration of XAI with clinical workflows. This section emphasizes the importance of tailoring explanations to the needs of physicians, ensuring the information is clear, actionable, and aligns with established medical knowledge. We discuss strategies for visualizing explanations in a user-friendly format that facilitates physician understanding and promotes informed clinical decision-making. Furthermore, the paper addresses the ethical considerations surrounding XAI in healthcare. We explore issues like fairness, bias, and potential misuse of explanations. Mitigating bias in deep learning models and ensuring explanations are not misinterpreted become crucial aspects of building trustworthy systems. Finally, the paper concludes by outlining the future directions of XAI for medical diagnosis. We discuss the ongoing research efforts to develop more robust and user-centric XAI methods specifically suited for the complexities of medical data and decision-making. By fostering collaboration among AI researchers, medical professionals, and ethicists, we can develop trustworthy deep learning models that empower physicians and ultimately lead to enhanced patient care.
Keywords: XAI, Healthcare, Deep Learning, HER, Medical Diagnostics.
Scope of the Article: Health Improvement Strategies