Explainable AI for Healthcare Diagnostics: A Multi-modal Approach Using Vision and Language Models

Abstract : The integration of artificial intelligence (AI) in healthcare diagnostics has shown tremendous potential in improving accuracy, efficiency, and accessibility. However, the opaque nature of many high-performing AI models, particularly deep learning architectures, limits their acceptance in critical healthcare settings. This paper proposes a novel explainable AI (XAI) framework that leverages multi-modal data—combining medical imaging and clinical text—to enhance diagnostic performance while providing transparent, interpretable outputs. By integrating state-of-the-art Vision Transformers (ViTs) and Large Language Models (LLMs) such as BioBERT and GPT-derived architectures, our system captures complex patterns from both radiological images and corresponding patient records. The proposed framework incorporates attention-based visualization techniques, saliency mapping, and natural language explanation generation to make the model’s decisions understandable to clinicians. Experiments conducted on benchmark datasets like MIMIC-CXR and CheXpert demonstrate improved diagnostic accuracy across multiple conditions, while the inclusion of explainability mechanisms fosters higher trust and acceptability among medical professionals. This work contributes toward the responsible deployment of AI in clinical environments, where interpretability is not just an add-on but a critical requirement for real-world adoption.

Keyword Explainable AI (XAI), Multi-modal Learning, Vision Transformers, Large Language Models, Medical Imaging, Clinical Text Analysis, Healthcare Diagnostics, Interpretability, Attention Mechanisms, Medical AI Ethics