{"ID":2854256,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.15866","arxiv_id":"2510.15866","title":"BiomedXPro: Prompt Optimization for Explainable Diagnosis with Biomedical Vision Language Models","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.","short_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 obse...","url_abs":"https://arxiv.org/abs/2510.15866","url_pdf":"https://arxiv.org/pdf/2510.15866v1","authors":"[\"Kaushitha Silva\",\"Mansitha Eashwara\",\"Sanduni Ubayasiri\",\"Ruwan Tennakoon\",\"Damayanthi Herath\"]","published":"2025-10-17T17:58:31Z","proceeding":"cs.CV","tasks":"[\"cs.CV\",\"cs.NE\"]","methods":"[\"Language Model\"]","has_code":false}