用大模型把机器检测结果自动转成医生能读的报告
Using Large Language Models To Translate Machine Results To Human Results
- 用YOLO检测胸片异常，再让大模型生成描述性报告
- 生成报告与真人报告语义相似度高，但流畅度稍弱
- 适合想快速生成初稿的医生或研究者使用
人工智能已大幅提升医学影像分析能力，计算机视觉系统在分类与检测任务中达到顶尖水平。然而，这些系统通常输出结构化结果，仍需放射科医生将其转化为完整报告。近期大语言模型（如GPT-4）的发展为这一问题提供了新思路：可将结构化发现转换为自然语言诊断报告。本研究提出一种集成管道，采用YOLOv5和YOLOv8进行胸部X光片异常检测，输出边界框与类别标签后，输入大语言模型生成描述性发现与临床摘要。对比YOLOv5与YOLOv8在检测精度、推理延迟及生成文本质量（以与真实报告的余弦相似度衡量）方面的表现。结果显示，AI生成报告与人类报告具有高度语义相似性；人工评估显示，GPT-4在清晰度上表现优异（4.88/5），但在写作风格流畅性上得分较低（2.81/5），表明当前系统虽具备临床准确性，但仍可从文风上与专业报告区分。
原文摘要 · Abstract (English)
Artificial intelligence (AI) has transformed medical imaging, with computer vision (CV) systems achieving state-of-the-art performance in classification and detection tasks. However, these systems typically output structured predictions, leaving radiologists responsible for translating results into full narrative reports. Recent advances in large language models (LLMs), such as GPT-4, offer new opportunities to bridge this gap by generating diagnostic narratives from structured findings. This study introduces a pipeline that integrates YOLOv5 and YOLOv8 for anomaly detection in chest X-ray images with a large language model (LLM) to generate natural-language radiology reports. The YOLO models produce bounding-box predictions and class labels, which are then passed to the LLM to generate descriptive findings and clinical summaries. YOLOv5 and YOLOv8 are compared in terms of detection accuracy, inference latency, and the quality of generated text, as measured by cosine similarity to ground-truth reports. Results show strong semantic similarity between AI and human reports, while human evaluation reveals GPT-4 excels in clarity (4.88/5) but exhibits lower scores for natural writing flow (2.81/5), indicating that current systems achieve clinical accuracy but remain stylistically distinguishable from radiologist-authored text.
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