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Updated: Sep 18, 2025

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乳房超声成像中的GPT-4o和专用AI:对准确性,一致性,局限性和诊断潜力的比较研究
Deniz Esin Tekcan Sanli1, Ahmet Necati Sanli2, Yildiz Buyukdereli Atadag3
1Department of Radiology, Faculty of Medicine, Gaziantep University, Gaziantep, Turkey.
概括
这项研究比较了人工智能工具ChatGPT和乳房超声波助手用于分析乳房超声波图像. 虽然乳房超声波助手显示了更好的整体性能,但AI工具都不能可靠地匹配临床使用的放射科医生评估.
科学领域:
- 医疗成像中的人工智能
- 放射学和成像分析分析.
- 乳腺癌的诊断 乳腺癌的诊断
背景情况:
- 人工智能 (AI) 在医学图像分析方面表现有前途.
- 对特定的诊断任务评估像ChatGPT这样的AI工具至关重要.
- 乳房超声波的解释需要准确区分良性和恶性病变.
研究的目的:
- 评估ChatGPT和乳房超声波助手 (BUH) 在分析乳房超声波图像方面的能力.
- 评估他们区分良性和恶性乳腺病变的能力.
- 将AI性能与经验丰富的放射科医生的共识进行比较.
主要方法:
- 通过GPT-4o和BUH分析了232张经遗传病理学确认的乳房超声波图像 (133张恶性,99张良性).
- 人工智能工具使用美国放射学学院 (ACR) 的BI-RADS乳腺超声波词典标准进行了提示.
- 人工智能输出与两位经验丰富的放射科医生的关于病变特征和BI-RADS分类的共识评估进行了比较.
主要成果:
- 乳房超声波助手 (BUH) 显示出更高卡帕值 (P<.001) 的优越整体性能.
- 对BUH (κ: 0.387-0.755) 和GPT-4o (κ: 0.317-0.803) 的放射科医生的总体一致性相似.
- GPT-4o的BI-RADS类别一致性略高 (69.4%对65.9%),而BUH在区分良性病变和恶性病变方面略好 (67.7%对65.9%).
结论:
- 这两种AI工具在乳房超声波图像分析中表现中等到良好.
- 与放射科医生评估和BI-RADS分类的有限兼容性表明早期临床应用.
- 目前用于乳房超声波解释的人工智能工具被认为对广泛的临床使用是不可靠的.
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