MedDiT：一种用于虚拟模拟病人动态医学图像生成的知识控制扩散Transformer框架
MedDiT: A Knowledge-Controlled Diffusion Transformer Framework for Dynamic Medical Image Generation in Virtual Simulated Patient
AI总结：
本文提出MedDiT框架，通过集成病人知识图谱控制大语言模型行为并结合扩散Transformer模型，动态生成与症状相符的医学图像，为医学教育提供低成本、多样化的交互式诊断训练平台。
摘要：
医学教育严重依赖模拟病人（SPs）为学生提供安全的环境来练习临床技能，包括医学图像分析。然而，招募合格SPs的高成本和缺乏多样化的医学影像数据集带来了重大挑战。为了解决这些问题，本文介绍了MedDiT，这是一种新颖的知识控制对话框架，能够动态生成与模拟病人症状相符的合理医学图像，从而实现多样化的诊断技能训练。具体而言，MedDiT集成了各种病人知识图谱（KGs），这些图谱描述了病人的属性和症状，以动态提示大型语言模型（LLMs）的行为并控制病人特征，从而减少医学对话中的幻觉。此外，还结合了一个经过良好调优的扩散Transformer（DiT）模型，根据KG中指定的病人属性生成医学图像。本文通过实际演示展示了MedDiT的能力，展示了其在各种模拟病人案例中表现并生成相应医学图像的能力。这可以为学生提供丰富且互动的学习体验，通过为未来的医疗保健专业人员提供沉浸式模拟平台来推进医学教育。这项工作阐明了将LLM、KG和DiT等先进技术纳入教育应用的可行性，突出了它们在解决基于模拟病人的医学教育所面临挑战方面的潜力。
英文摘要：
Medical education relies heavily on Simulated Patients (SPs) to provide a safe environment for students to practice clinical skills, including medical image analysis. However, the high cost of recruiting qualified SPs and the lack of diverse medical imaging datasets have presented significant challenges. To address these issues, this paper introduces MedDiT, a novel knowledge-controlled conversational framework that can dynamically generate plausible medical images aligned with simulated patient symptoms, enabling diverse diagnostic skill training. Specifically, MedDiT integrates various patient Knowledge Graphs (KGs), which describe the attributes and symptoms of patients, to dynamically prompt Large Language Models' (LLMs) behavior and control the patient characteristics, mitigating hallucination during medical conversation. Additionally, a well-tuned Diffusion Transformer (DiT) model is incorporated to generate medical images according to the specified patient attributes in the KG. In this paper, we present the capabilities of MedDiT through a practical demonstration, showcasing its ability to act in diverse simulated patient cases and generate the corresponding medical images. This can provide an abundant and interactive learning experience for students, advancing medical education by offering an immersive simulation platform for future healthcare professionals. The work sheds light on the feasibility of incorporating advanced technologies like LLM, KG, and DiT in education applications, highlighting their potential to address the challenges faced in simulated patient-based medical education.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
