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This paper introduces CSEval, a framework for evaluating the clinical semantic alignment between text prompts and generated medical images, addressing the limitations of existing metrics focused on realism and diversity. CSEval uses language models to identify semantic inconsistencies related to anatomical location and pathology, demonstrating a correlation with expert clinical judgment. The framework offers a scalable method for assessing the clinical reliability of generated medical images, crucial for the safe deployment of text-to-image models in healthcare.
Current text-to-image evaluation metrics miss critical clinical errors, but CSEval uses language models to catch them.
Text-to-image generation has been increasingly applied in medical domains for various purposes such as data augmentation and education. Evaluating the quality and clinical reliability of these generated images is essential. However, existing methods mainly assess image realism or diversity, while failing to capture whether the generated images reflect the intended clinical semantics, such as anatomical location and pathology. In this study, we propose the Clinical Semantics Evaluator (CSEval), a framework that leverages language models to assess clinical semantic alignment between the generated images and their conditioning prompts. Our experiments show that CSEval identifies semantic inconsistencies overlooked by other metrics and correlates with expert judgment. CSEval provides a scalable and clinically meaningful complement to existing evaluation methods, supporting the safe adoption of generative models in healthcare.
