Proof-of-TBI——基于微调视觉语言模型联盟与OpenAI-o3推理LLM的轻度创伤性脑损伤（TBI）预测医疗诊断支持系统
Proof-of-TBI -- Fine-Tuned Vision Language Model Consortium and OpenAI-o3 Reasoning LLM-Based Medical Diagnosis Support System for Mild Traumatic Brain Injury (TBI) Prediction
- Old Dominion University（旧 Dominion 大学）
- McDonald Army Health Center（麦克唐纳陆军医疗中心）
- BRAINBox Solutions（BRAINBox 解决方案）
- UVA Health（弗吉尼亚大学健康系统）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对轻度脑损伤医学影像诊断困难的问题，提出Proof-of-TBI系统，结合五个微调视觉语言模型与OpenAI-o3推理LLM，通过LLM智能体编排实现高准确、透明的自动化诊断，为TBI预测首次实现此类模型集成。
AI中文摘要：
轻度创伤性脑损伤（TBI）检测面临重大挑战，因为医学影像中的症状表现微妙且往往具有模糊性，使得准确诊断成为一项复杂的任务。为了应对这些挑战，我们提出了Proof-of-TBI，这是一种医疗诊断支持系统，它将多个微调的视觉语言模型与OpenAI-o3推理大型语言模型（LLM）集成在一起。我们的方法使用带有标签的TBI MRI扫描数据集微调多个视觉语言模型，训练它们有效地诊断TBI症状。这些模型的预测结果通过基于共识的决策过程进行聚合。该系统利用OpenAI-o3推理LLM（一个展现出卓越推理性能的模型）评估所有微调视觉语言模型的预测，以产生最准确的最终诊断。LLM智能体编排视觉语言模型与推理LLM之间的交互，以透明、可靠和自动化的方式管理最终决策过程。这种端到端的决策工作流将视觉语言模型联盟与OpenAI-o3推理LLM相结合，并由LLM智能体通过自定义提示工程提供支持。该拟议平台的原型是与位于弗吉尼亚州纽波特纽斯的美国陆军医疗研究团队合作开发的，整合了五个微调的视觉语言模型。结果证明了将微调视觉语言模型的输入与OpenAI-o3推理LLM相结合以创建用于轻度TBI预测的稳健、安全且高度准确的诊断系统的变革潜力。据我们所知，这项研究代表了将微调视觉语言模型与推理LLM集成用于TBI预测任务的首次应用。
英文摘要：
Mild Traumatic Brain Injury (TBI) detection presents significant challenges due to the subtle and often ambiguous presentation of symptoms in medical imaging, making accurate diagnosis a complex task. To address these challenges, we propose Proof-of-TBI, a medical diagnosis support system that integrates multiple fine-tuned vision-language models with the OpenAI-o3 reasoning large language model (LLM). Our approach fine-tunes multiple vision-language models using a labeled dataset of TBI MRI scans, training them to diagnose TBI symptoms effectively. The predictions from these models are aggregated through a consensus-based decision-making process. The system evaluates the predictions from all fine-tuned vision language models using the OpenAI-o3 reasoning LLM, a model that has demonstrated remarkable reasoning performance, to produce the most accurate final diagnosis. The LLM Agents orchestrates interactions between the vision-language models and the reasoning LLM, managing the final decision-making process with transparency, reliability, and automation. This end-to-end decision-making workflow combines the vision-language model consortium with the OpenAI-o3 reasoning LLM, enabled by custom prompt engineering by the LLM agents. The prototype for the proposed platform was developed in collaboration with the U.S. Army Medical Research team in Newport News, Virginia, incorporating five fine-tuned vision-language models. The results demonstrate the transformative potential of combining fine-tuned vision-language model inputs with the OpenAI-o3 reasoning LLM to create a robust, secure, and highly accurate diagnostic system for mild TBI prediction. To the best of our knowledge, this research represents the first application of fine-tuned vision-language models integrated with a reasoning LLM for TBI prediction tasks.
