Hybrid AI Framework for Accurate Diagnostics: Merging Deep Learning with Rule-Based and Explainable Techniques Across Imaging Modalities

Abstract

This paper presents a robust hybrid artificial intelligence (AI) framework designed to improve diagnostic accuracy in medical imaging tests such as magnetic resonance imaging (MRI), computed tomography (CT), and positron emission tomography (PET) scans. The enhancement is achieved by combining deep learning methods with rule-based reasoning and model-agnostic explainability techniques. The proposed hybrid architecture integrates convolutional neural networks (CNNs) and transformer models for feature extraction, while incorporating expert-defined rule-based logic to strengthen interpretability and ensure consistency in decision-making. To improve transparency, model-agnostic explainability approaches such as SHAP (Shapley Additive Explanations) and LIME (Local Interpretable Model-Agnostic Explanations) are applied, offering deeper insights into AI-driven diagnoses. This integration addresses critical clinical concerns related to trust, traceability, and adoption of AI systems. Experimental evaluations conducted on benchmark medical imaging datasets achieved an accuracy of 94.2%, which is 3.8% higher than conventional deep learning approaches, demonstrating improved robustness. Additionally, the proposed system enhances clinical interpretability, with a 21% increase in explainability ratings provided by healthcare professionals. These findings highlight the novelty and clinical relevance of hybrid AI models by combining automation with decision support, thereby promoting greater trust and adoption of AI in diagnostic workflows across healthcare facilities.

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