With the rapid advancement of large language models (LLMs), foundational models (FMs) have seen significant advancements. Healthcare is one of the most crucial application areas for these FMs, given the significant time and effort required for physicians to analyze large volumes of patient data. Recent efforts have focused on adapting multimodal FMs to the medical domain through techniques like instruction-tuning, leading to the development of medical foundation models (MFMs). However, these approaches typically require large amounts of training data to effectively adapt models to the medical field. Moreover, most existing models are trained on English datasets, limiting their practicality in non-English-speaking regions where healthcare professionals and patients are not always fluent in English. The need for translation introduces additional costs and inefficiencies. To address these challenges, we propose a \textbf{J}apanese \textbf{Radi}ology report generation model enhanced by \textbf{Evo}lutionary optimization of model merging (JRadiEvo). This is the first attempt to extend a non-medical vision-language foundation model to the medical domain through evolutionary optimization of model merging. We successfully created a model that generates accurate Japanese reports from X-ray images using only 50 translated samples from publicly available data. This model, developed with highly efficient use of limited data, outperformed leading models from recent research trained on much larger datasets. Additionally, with only 8 billion parameters, this relatively compact foundation model can be deployed locally within hospitals, making it a practical solution for environments where APIs and other external services cannot be used due to strict privacy and security requirements.

翻译：随着大语言模型（LLM）的快速发展，基础模型（FM）取得了显著进步。鉴于医生分析大量患者数据需要耗费大量时间和精力，医疗保健是这些基础模型最关键的应用领域之一。近期的研究重点是通过指令微调等技术将多模态基础模型适配到医疗领域，从而推动了医疗基础模型（MFM）的发展。然而，这些方法通常需要大量的训练数据才能有效地使模型适应医疗领域。此外，现有模型大多基于英语数据集进行训练，这限制了其在非英语地区的实用性，因为当地的医疗专业人员和患者并不总是精通英语。翻译需求会带来额外的成本与效率损失。为应对这些挑战，我们提出了一种通过模型融合的进化优化增强的日语放射学报告生成模型（JRadiEvo）。这是首次尝试通过模型融合的进化优化，将非医疗领域的视觉-语言基础模型扩展到医疗领域。我们仅使用公开数据中翻译得到的50个样本，就成功创建了一个能够从X射线图像生成准确日语报告的模型。该模型在极高效地利用有限数据的情况下开发而成，其性能优于近期研究中基于更大规模数据集训练的主流模型。此外，该模型仅有80亿参数，作为一个相对紧凑的基础模型，可以部署在医院本地，这为那些因严格的隐私和安全要求而无法使用API及其他外部服务的环境提供了一个实用的解决方案。