Explainable AI (XAI) in Healthcare: Building Trust in Medical Diagnosis Systems

Prajakta Sudhir Khade

International Journal of Scientific Research in Science and Technology, 2025, vol. 12, issue 6, 07-22

Abstract: Artificial Intelligence (AI) has emerged as a transformative force in healthcare, enabling highly accurate diagnoses through advanced medical imaging modalities such as computed tomography (CT), magnetic resonance imaging (MRI), and X-rays. Despite the superior performance of deep learning models, their “black-box” nature poses challenges for clinical adoption due to limited transparency and interpretability. Explainable AI (XAI) addresses these limitations by elucidating the decision-making processes of AI systems, thereby enhancing trust, accountability, and user confidence in medical applications. Techniques such as SHAP (SHapley Additive Explanations), LIME (Local Interpretable Model-Agnostic Explanations), and Grad-CAM (Gradient-weighted Class Activation Mapping) have become integral in interpreting model predictions for critical tasks like tumor detection and pulmonary disease classification. By facilitating model transparency and interpretability, XAI empowers healthcare professionals to validate AI-driven insights, ensuring safer and more reliable decision-making. Furthermore, XAI supports the ethical deployment of AI by promoting fairness, regulatory compliance, and effective human-AI collaboration. As AI continues to advance within clinical environments, XAI remains pivotal in fostering trust and ensuring the responsible, interpretable, and effective integration of AI technologies in healthcare.

Keywords: Explainable Artificial Intelligence (XAI); Medical Imaging; Healthcare AI; Model Interpretability; Deep Learning in Diagnosis (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://ijsrst.com/home/article/view/IJSRST25126288 Abstract page (text/html)
https://ijsrst.com/home/article/download/IJSRST25126288/IJSRST25126288 Full text (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:etm:ijsrst:v12:y2025:i6:id:1250

DOI: 10.32628/IJSRST25126288

Access Statistics for this article

More articles in International Journal of Scientific Research in Science and Technology from Technoscience Academy
Bibliographic data for series maintained by Pankaj Sharma ().