This research introduces a novel Vision-Amplified Semantic Entropy (VASE) approach to detect and prevent hallucinations in medical AI systems that answer questions about visual data.

• Combines language model uncertainty with visual feature analysis to identify when AI responses contradict medical images
• Achieves superior detection performance across multiple medical datasets and models
• Introduces a new clinical validity benchmark for evaluating hallucination detection in medical contexts
• Enables safer clinical implementation by identifying potentially harmful AI responses

This breakthrough is critical for medical applications where AI hallucinations could lead to misdiagnosis or incorrect treatment decisions, significantly enhancing trustworthiness for real-world clinical adoption.

Vision-Amplified Semantic Entropy for Hallucination Detection in Medical Visual Question Answering