The integration of Computer-Assisted Diagnosis (CAD) with Large Language Models (LLMs) holds great potential in clinical applications, specifically in the roles of virtual family doctors and clinic assistants. However, current works in this field are plagued by limitations, specifically a restricted scope of applicable image domains and the provision of unreliable medical advice. This restricts their overall processing capabilities. Furthermore, the mismatch in writing style between LLMs and radiologists undermines their practical usefulness. To tackle these challenges, we introduce ChatCAD+, which is designed to be universal and reliable. It is capable of handling medical images from diverse domains and leveraging up-to-date information from reputable medical websites to provide reliable medical advice. Additionally, it incorporates a template retrieval system that improves report generation performance via exemplar reports. This approach ensures greater consistency with the expertise of human professionals. The source code is available at https://github.com/zhaozh10/ChatCAD.

翻译：将计算机辅助诊断（CAD）与大语言模型（LLMs）相结合在临床应用中潜力巨大，尤其是在虚拟家庭医生和临床助理角色方面。然而，当前该领域的研究存在局限性，具体表现为适用图像领域的范围受限以及提供的医疗建议不可靠，这制约了其整体处理能力。此外，LLM与放射科医生写作风格的不匹配也削弱了其实用价值。为应对这些挑战，我们提出ChatCAD+，该系统被设计为通用且可靠。它能够处理来自不同领域的医学图像，并利用来自权威医疗网站的最新信息提供可靠的医疗建议。同时，系统整合了模板检索机制，通过示例报告提升报告生成性能。该方法确保了与人类专家专业知识的高度一致性。源代码已开源在https://github.com/zhaozh10/ChatCAD。