International Journal For Multidisciplinary Research

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Explainable AI for Clinical Decision Support: SHAP and Grad-CAM in Text- and Image-Based Medical Predictions

Author(s) Rohit Kshirsagar, Rishabh Kothari, Parth Lhase, Dr. Shagufta Sheikh
Country India
Abstract The integration of Artifcial Intelligence (AI), par-
ticularly deep learning, into clinical workfows promises to revo-
lutionize medical diagnostics. High-performance models can now
analyze medical images and clinical text with accuracy rivaling,
and sometimes exceeding, human experts. However, the “black-
box” nature of these complex models is a signifcant barrier to
their widespread clinical adoption. Physicians require transpar-
ent and interpretable reasoning to trust AI-driven predictions for
high-stakes decisions. This survey addresses this critical need by
providing a focused review of Explainable AI (XAI) techniques
tailored for medical applications. We move beyond general
discussions of interpretability to conduct a detailed analysis of
two of the most prominent and practical post-hoc XAI methods:
SHapley Additive exPlanations (SHAP) for text-based predictions
from electronic health records, and Gradient-weighted Class
Activation Mapping (Grad-CAM) for image-based predictions
from modalities like CT and MRI. We introduce a taxonomy
of XAI and situate these methods within it, reviewing their
mechanisms, applications, and limitations. By focusing on this
multimodal approach, this paper serves as a practical guide for
researchers and clinicians aiming to develop, evaluate, and deploy
the next generation of trustworthy, transparent, and effective AI-
powered clinical decision support systems.
Field Computer Applications
Published In Volume 7, Issue 6, November-December 2025
Published On 2025-11-13
DOI https://doi.org/10.36948/ijfmr.2025.v07i06.60343
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