Mixture of Expert Tuning (MoE-Tuning) has effectively enhanced the performance of general MLLMs with fewer parameters, yet its application in resource-limited medical settings has not been fully explored. To address this gap, we developed MoE-TinyMed, a model tailored for medical applications that significantly lowers parameter demands. In evaluations on the VQA-RAD, SLAKE, and Path-VQA datasets, MoE-TinyMed outperformed LLaVA-Med in all Med-VQA closed settings with just 3.6B parameters. Additionally, a streamlined version with 2B parameters surpassed LLaVA-Med's performance in PathVQA, showcasing its effectiveness in resource-limited healthcare settings.

翻译：混合专家微调（MoE-Tuning）已有效提升了通用多模态大语言模型在参数量更少时的性能表现，但其在资源受限医疗场景中的应用尚未得到充分探索。为填补这一空白，我们开发了专为医学应用定制的MoE-TinyMed模型，该模型显著降低了参数需求。在VQA-RAD、SLAKE和Path-VQA数据集上的评估中，MoE-TinyMed以3.6B参数在所有Med-VQA封闭式设置中均优于LLaVA-Med。此外，其精简版（2B参数）在PathVQA任务中超越LLaVA-Med的表现，充分证明了该模型在医疗资源受限场景中的有效性。