NEURO-GUARD: Neuro-Symbolic Generalization and Unbiased Adaptive Routing for Diagnostics -- Explainable Medical AI
Midhat Urooj, Ayan Banerjee, Sandeep Gupta

TL;DR
NEURO-GUARD is a neuro-symbolic vision framework that combines vision transformers with language-driven reasoning and clinical knowledge to improve interpretability, accuracy, and domain robustness in medical image diagnosis.
Contribution
It introduces a knowledge-guided, retrieval-augmented generation mechanism integrating language models with vision transformers for explainable medical AI.
Findings
Improves diabetic retinopathy classification accuracy by 6.2% over baseline
Achieves 5% better domain generalization across datasets
Demonstrates robustness in MRI seizure detection
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…
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Taxonomy
TopicsRetinal Imaging and Analysis · Machine Learning in Healthcare · Explainable Artificial Intelligence (XAI)
