Recent advancements in artificial intelligence (AI) have precipitated significant breakthroughs in healthcare, particularly in refining diagnostic procedures. However, previous studies have often been constrained to limited functionalities. This study introduces MiniGPT-Med, a vision-language model derived from large-scale language models and tailored for medical applications. MiniGPT-Med demonstrates remarkable versatility across various imaging modalities, including X-rays, CT scans, and MRIs, enhancing its utility. The model is capable of performing tasks such as medical report generation, visual question answering (VQA), and disease identification within medical imagery. Its integrated processing of both image and textual clinical data markedly improves diagnostic accuracy. Our empirical assessments confirm MiniGPT-Med's superior performance in disease grounding, medical report generation, and VQA benchmarks, representing a significant step towards reducing the gap in assisting radiology practice. Furthermore, it achieves state-of-the-art performance on medical report generation, higher than the previous best model by 19\% accuracy. MiniGPT-Med promises to become a general interface for radiology diagnoses, enhancing diagnostic efficiency across a wide range of medical imaging applications.

翻译：人工智能（AI）的最新进展在医疗保健领域引发了重大突破，特别是在优化诊断流程方面。然而，既往研究往往局限于有限的功能。本研究介绍了MiniGPT-Med，这是一个源自大规模语言模型并针对医疗应用定制的视觉-语言模型。MiniGPT-Med在包括X射线、CT扫描和MRI在内的多种成像模态上展现出卓越的通用性，从而增强了其实用性。该模型能够执行医疗报告生成、视觉问答（VQA）以及医学影像中的疾病识别等任务。其对图像和文本临床数据的集成处理显著提高了诊断准确性。我们的实证评估证实了MiniGPT-Med在疾病定位、医疗报告生成和VQA基准测试中的卓越性能，这标志着在协助放射学实践方面迈出了重要一步。此外，它在医疗报告生成上达到了最先进的性能，准确率比先前的最佳模型高出19%。MiniGPT-Med有望成为放射学诊断的通用接口，从而提升广泛医学影像应用中的诊断效率。