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GPT-4与放射科医生:谁在报告质量层面上更好地推进中瘤分类? 一个队列研究研究

Ru Wen¹, Xiaoming Li¹, Kang Chen¹

¹7T Magnetic Resonance Translational Medicine Research Center, Department of Radiology, Southwest Hospital, Army Medical University (Third Military Medical University), Chongqing, China.

International journal of surgery (London, England)

|August 11, 2025

概括

在分类中瘤方面,GPT-4的准确性与高级放射科医生的准确性相当. 它在低质量的报告中脱而出,并改善淋巴瘤诊断,提供了提高诊断性能的潜力.

科学领域:

医疗成像中的人工智能
放射学和诊断瘤学诊断瘤学
在医疗保健中的自然语言处理.

背景情况:

准确的中瘤分类对于有效的治疗计划至关重要.
放射科医生的诊断性能受到经验和报告质量的影响.
诊断准确度的变化需要先进的工具来评估中瘤.

研究的目的:

通过使用放射学报告来评估GPT-4对中瘤的诊断准确性.
为了比较GPT-4的性能与不同经验水平的放射科医生.
为了评估GPT-4在不同放射性报告质量的性能.

主要方法:

通过CT和病理学诊断的1494例中瘤病例的回顾性分析.
放射学报告的分类为低质量,中质量和高质量.
评估六名放射科医生和GPT-4的报告,并根据报告质量和瘤类型进行绩效分析.

主要成果:

GPT-4的整体诊断准确率达到73.3%,相当于高级放射科医生 (74.3%).
在低质量报告方面,GPT-4的表现优于高级放射科医生 (60.8%对51.1%).
在诊断淋巴瘤方面,GPT-4显示出更高的准确性 (75.4%与60.4%相比),并且对瘤和神经性瘤的性能相当.

结论:

GPT-4证明了与高级放射科医师同等的解释准确性.
人工智能模型在解释低质量的报告和诊断特定瘤类型 (如淋巴瘤) 中表现出色.
GPT-4具有显著的潜力,可以在具有挑战性的中瘤分类场景中增强诊断能力.

关键词:

在 GPT-4 中使用.诊断的准确性诊断的准确性在中的瘤中.放射学报告放射学报告瘤的分类瘤的分类

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