用双模型共识提升X光诊断可信度，准确率最高达91.3%。
Fusion-Augmented Large Language Models: Boosting Diagnostic Trustworthiness via Model Consensus
- 基于输出相似性构建双大模型共识机制
- 多模态输入下诊断准确率达91.3%（原单模型最高76.9%）
- 适合希望提升AI诊断可信度的临床研究者
本研究提出一种新型多模型融合框架，利用ChatGPT与Claude两个先进大语言模型，在CheXpert数据集上提升胸部X光片解读的可靠性。从224,316张胸片中随机选取234例放射科医生标注样本，仅用图像提示时，ChatGPT和Claude的诊断准确率分别为62.8%和76.9%。采用95%输出相似性阈值的共识方法后，准确率提升至77.6%。为进一步评估多模态输入效果，基于MIMIC-CXR模板生成合成临床记录，对50例随机样本进行图像与文本联合评估，此时ChatGPT准确率达到84%，Claude为76%，共识准确率达91.3%。在两种实验条件下，基于一致性的融合策略始终优于单一模型。结果表明，融合互补模态并使用输出级共识可显著提升AI辅助放射诊断的可信度与临床价值，且计算开销极小。
原文摘要 · Abstract (English)
This study presents a novel multi-model fusion framework leveraging two state-of-the-art large language models (LLMs), ChatGPT and Claude, to enhance the reliability of chest X-ray interpretation on the CheXpert dataset. From the full CheXpert corpus of 224,316 chest radiographs, we randomly selected 234 radiologist-annotated studies to evaluate unimodal performance using image-only prompts. In this setting, ChatGPT and Claude achieved diagnostic accuracies of 62.8% and 76.9%, respectively. A similarity-based consensus approach, using a 95% output similarity threshold, improved accuracy to 77.6%. To assess the impact of multimodal inputs, we then generated synthetic clinical notes following the MIMIC-CXR template and evaluated a separate subset of 50 randomly selected cases paired with both images and synthetic text. On this multimodal cohort, performance improved to 84% for ChatGPT and 76% for Claude, while consensus accuracy reached 91.3%. Across both experimental conditions, agreement-based fusion consistently outperformed individual models. These findings highlight the utility of integrating complementary modalities and using output-level consensus to improve the trustworthiness and clinical utility of AI-assisted radiological diagnosis, offering a practical path to reduce diagnostic errors with minimal computational overhead.
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