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在放射学中建立基础模型? 对GPT-4V的多模式和多解剖区域能力的定量评估
Quirin D Strotzer1, Felix Nieberle1, Laura S Kupke1
1From the Institute of Radiology (Q.D.S., L.S.K., G.N., A.K.M., S.M., I.E., J.R., C.W., C.S., O.W.H., A.S.) and Department of Cranio- and Maxillofacial Surgery (F.N.), University of Regensburg Medical Center, Franz-Josef-Strauss-Allee 11, 93053 Regensburg, Germany; Department of Radiology, Division of Neuroradiology, Massachusetts General Hospital, Harvard Medical School, Boston, Mass (Q.D.S.); Department of Radiology, Bayreuth Medical Center, Bayreuth, Germany (M.S.); Center of Neuroradiology, medbo District Hospital and University Medical Center Regensburg, Regensburg, Germany (I.W., C.W.); and Department of Radiology, Donaustauf Hospital, Donaustauf, Germany (O.W.H.).
GPT-4V可以识别医疗图像类型和位置,但难以检测和分类异常. 这种大型视觉语言模型在解释放射图像时显示出高的错误阳性率.
科学领域:
- 人工智能的人工智能
- 医疗成像医学成像
- 放射学 放射学是一门学科.
背景情况:
- 大型语言模型 (LLM) 在处理医疗文本方面表现有前途.
- 视觉语言模型GPT-4V在医学成像分析方面具有潜力.
- 需要对GPT-4V进行定量评估,以解释放射图像.
研究的目的:
- 量化评估GPT-4V在解释看不见的放射图像方面的性能.
- 评估GPT-4V的诊断报告的准确性,敏感性和特异性.
- 为了比较GPT-4V与人类放射科医生的性能.
主要方法:
- 在神经放射学,心胸和肌肉骨放射学中对单个异常和健康对照图像进行回顾性分析.
- 通过API进行GPT-4V解释,用于自由文本报告和二进制分类任务.
- 性能指标 (准确度,灵敏度,特异性) 与一年级住院医生和四名经过董事会认证的放射科医生相比.
主要成果:
- GPT-4V在识别成像模式方面实现了100%的准确性,在解剖区域方面达到99.2%.
- 自由文本报告的诊断准确性差异很大 (肺胸炎的0%到脑瘤的90%).
- 与人类阅读器相比,GPT-4V显示出高的错误阳性率 (86.5%为自由文本,67.7%为二进制分类) 和较低的灵敏度/特异性.
结论:
- 最初的GPT-4V版本可以识别医疗图像内容,模式和解剖学.
- 目前GPT-4V无法可靠地检测,分类或排除放射图像中的异常.
- 为了使GPT-4V成为诊断放射学中可靠的工具,需要进一步开发.
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