BiomedXPro: Prompt Optimization for Explainable Diagnosis with Biomedical Vision Language Models

• URL: http://arxiv.org/abs/2510.15866v1
• Date: Fri, 17 Oct 2025 17:58:31 GMT
• Title: BiomedXPro: Prompt Optimization for Explainable Diagnosis with Biomedical Vision Language Models
• Abstract summary: We introduce BiomedXPro, a framework that generates a diverse ensemble of interpretable natural-language prompts for disease diagnosis.<n> Experiments show that BiomedXPro consistently outperforms state-of-the-art prompt-tuning methods.<n>By producing a diverse ensemble of interpretable prompts, BiomedXPro provides a verifiable basis for model predictions.
• Score: 0.16419687521433918
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The clinical adoption of biomedical vision-language models is hindered by prompt optimization techniques that produce either uninterpretable latent vectors or single textual prompts. This lack of transparency and failure to capture the multi-faceted nature of clinical diagnosis, which relies on integrating diverse observations, limits their trustworthiness in high-stakes settings. To address this, we introduce BiomedXPro, an evolutionary framework that leverages a large language model as both a biomedical knowledge extractor and an adaptive optimizer to automatically generate a diverse ensemble of interpretable, natural-language prompt pairs for disease diagnosis. Experiments on multiple biomedical benchmarks show that BiomedXPro consistently outperforms state-of-the-art prompt-tuning methods, particularly in data-scarce few-shot settings. Furthermore, our analysis demonstrates a strong semantic alignment between the discovered prompts and statistically significant clinical features, grounding the model's performance in verifiable concepts. By producing a diverse ensemble of interpretable prompts, BiomedXPro provides a verifiable basis for model predictions, representing a critical step toward the development of more trustworthy and clinically-aligned AI systems.

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