Advanced International Journal for Research

E-ISSN: 3048-7641   •   Impact Factor: 9.11

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Neuro-Symbolic Intelligence in Medical Imaging: A Systematic Review of Explainable Chest X-Ray Analysis

Author(s) Ms. Sakshi Sawate, Dr. Leena Patil, Dr. Namrata Khade, Dr. Vaishnavi Ganesh, Dr. Himanshu Taiwade
Country India
Abstract The increasing use of deep learning in medical imaging has significantly improved diagnostic accuracy but raised concerns regarding interpretability and trustworthiness. Conventional convolutional artificial neural networks (CNNs) function as "black-box" models that provide little information about how they make decisions. This study combines the logical accessibility of symbolic reasoning with the perceptual power of neural networks to offer a neuro-symbolic structure for an straightforward medical diagnosis utilizing chest X-ray pictures. The CNN component extracts and classifies thoracic abnormalities, while the symbolic reasoning layer integrates medical rules and ontologies to produce interpretable, rule-based diagnostic conclusions. The hybrid approach ensures that diagnostic outputs are both data-driven and clinically meaningful, aligning with medical guidelines. The proposed system enhances transparency, reduces diagnostic bias, and supports clinicians with human-understandable explanations. Experimental evaluation demonstrates improved accuracy and interpretability for detecting pneumonia, tuberculosis, and lung abnormalities. This study contributes toward building trustworthy, ethical, and explainable artificial intelligence solutions for healthcare applications.
Keywords Neuro-Symbolic Artificial Intelligence, Explainable Medical Diagnosis, Chest X-Ray Image Analysis, Deep Learning and Symbolic Reasoning, Interpretable Healthcare Systems etc.
Published In Volume 7, Issue 2, March-April 2026
Published On 2026-04-24
DOI https://doi.org/10.63363/aijfr.2026.v07i02.5167
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