NEURO-GUARD: Neuro-Symbolic Generalization and Unbiased Adaptive Routing for Diagnostics -- Explainable Medical AI

2025/12/20 by Mohd Urooj, Ayan Banerjee, Urooj, Midhat +3
Computer Science · Medicine · #Artificial Intelligence (cs.AI) #Computer Vision and Pattern Recognition (cs.CV) #Explainable Artificial Intelligence (XAI) #FOS: Computer and information sciences #Machine Learning in Healthcare #Retinal Imaging and Analysis

paper · doi:10.48550/arxiv.2512.18177

openalex publication_date 2025/12/20 · openalex created_date 2025/12/24 · openalex updated_date 2026/07/28

Abstract

Accurate yet interpretable image-based diagnosis remains a central challenge in medical AI, particularly in settings characterized by limited data, subtle visual cues, and high-stakes clinical decision-making. Most existing vision models rely on purely data-driven learning and produce black-box predictions with limited interpretability and poor cross-domain generalization, hindering their real-world clinical adoption. We present NEURO-GUARD, a novel knowledge-guided vision framework that integrates Vision Transformers (ViTs) with language-driven reasoning to improve performance, transparency, and domain robustness. NEURO-GUARD employs a retrieval-augmented generation (RAG) mechanism for self-verification, in which a large language model (LLM) iteratively generates, evaluates, and refines feature-extraction code for medical images. By grounding this process in clinical guidelines and expert knowledge, the framework progressively enhances feature detection and classification beyond purely data-driven baselines. Extensive experiments on diabetic retinopathy classification across four benchmark datasets APTOS, EyePACS, Messidor-1, and Messidor-2 demonstrate that NEURO-GUARD improves accuracy by 6.2% over a ViT-only baseline (84.69% vs. 78.4%) and achieves a 5% gain in domain generalization. Additional evaluations on MRI-based seizure detection further confirm its cross-domain robustness, consistently outperforming existing methods. Overall, NEURO-GUARD bridges symbolic medical reasoning with subsymbolic visual learning, enabling interpretable, knowledge-aware, and generalizable medical image diagnosis while achieving state-of-the-art performance across multiple datasets.

Citations

• Trustworthy Medical Imaging with Large Language Models: A Study of Hallucinations Across Modalities
• MedVH: Towards Systematic Evaluation of Hallucination for Large Vision Language Models in the Medical Context
• CARES: A Comprehensive Benchmark of Trustworthiness in Medical Vision Language Models
• LLM-Seg: Bridging Image Segmentation and Large Language Model Reasoning
• Robust and Interpretable Medical Image Classifiers via Concept Bottleneck Models
• ViperGPT: Visual Inference via Python Execution for Reasoning
• Visual Programming: Compositional visual reasoning without training
• MedCLIP: Contrastive Learning from Unpaired Medical Images and Text
• Learning Transferable Visual Models From Natural Language Supervision
• An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
• Concept Bottleneck Models
• A Unified Approach to Interpreting Model Predictions
• Very Deep Convolutional Networks for Large-Scale Image Recognition

Related

• MedXAI: A Retrieval-Augmented and Self-Verifying Framework for Knowledge-Guided Medical Image Analysis
• Single Domain Generalization in Diabetic Retinopathy: A Neuro-Symbolic Learning Approach
• Beyond Classification Accuracy: Neural-MedBench and the Need for Deeper Reasoning Benchmarks
• MedEyes: Learning Dynamic Visual Focus for Medical Progressive Diagnosis
• Med-R1: Reinforcement Learning for Generalizable Medical Reasoning in Vision-Language Models