Diagnostic performance of artificial intelligence ChatGPT-4V in differentiating benign and malignant breast lesions on ultrasound

HE Yuqing; WU Zizheng; GUO Shuai; GAO Qinzhuang; LI Hui; YANG Baisheng

doi:10.19732/j.cnki.2096-6210.2026.01.008

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Diagnostic performance of artificial intelligence ChatGPT-4V in differentiating benign and malignant breast lesions on ultrasound

Article | 更新时间：2026-03-25

- Diagnostic performance of artificial intelligence ChatGPT-4V in differentiating benign and malignant breast lesions on ultrasound
- Oncoradiology Vol. 35, Issue 1, Pages: 57-63(2026)
- 作者机构：
  
  1.秦皇岛市第一医院超声医学科，河北秦皇岛 066001
  2.秦皇岛市卫生学校，河北秦皇岛 066001
- 作者简介：
  
  WU Zizheng　E-mail: wwzzh890415@163.com
- 基金信息：
  
  Medical Science Research Project of Hebei(20231893);Science and Technology Research and Development Program of Qinhuangdao(202301A199)
- DOI：10.19732/j.cnki.2096-6210.2026.01.008
  CLC： R737.9;R445.1
- Received：15 August 2025，
  
  Revised：2026-01-05，
  
  Published：28 February 2026
- 稿件说明：
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贺玉卿, 吴梓政, 郭　帅, 等. 人工智能ChatGPT-4V在乳腺超声病灶良恶性鉴别中的诊断效能［J］肿瘤影像学, 2026, 35(1): 57-63.

HE Y Q, WU Z Z, GUO S,Citation: et al. Diagnostic performance of artificial intelligence ChatGPT-4V in differentiating benign and malignant breast lesions on ultrasound［J］. Oncoradiology, 2026, 35(1): 57-63.
贺玉卿, 吴梓政, 郭　帅, 等. 人工智能ChatGPT-4V在乳腺超声病灶良恶性鉴别中的诊断效能［J］肿瘤影像学, 2026, 35(1): 57-63. DOI： 10.19732/j.cnki.2096-6210.2026.01.008.

HE Y Q, WU Z Z, GUO S,Citation: et al. Diagnostic performance of artificial intelligence ChatGPT-4V in differentiating benign and malignant breast lesions on ultrasound［J］. Oncoradiology, 2026, 35(1): 57-63. DOI： 10.19732/j.cnki.2096-6210.2026.01.008.

摘要

目的

评估ChatGPT-4V在乳腺超声病灶良恶性判读中的诊断效能，并与低年资及高年资医师进行比较，探讨其辅助诊断的可行性。

方法

回顾并纳入2024年1月—2025年6月秦皇岛市第一医院乳腺病变患者，以病理学检查结果为金标准，由ChatGPT-4V、2名低年资（3~5年工作经验）及2名高年资医师（>10年工作经验），采用盲法独立判读超声图像。记录灵敏度、特异度、准确度、受试者工作特征（receiver operating characteristic curve，ROC）曲线的曲线下面积（area under curve，AUC），并以乳腺影像报告和数据系统（Breast Imaging Reporting and Data System，BI-RADS）为标准评估形状、边界、回声类型、后方回声、钙化特征识别准确度。采用McNemar检验比较准确度，DeLong检验比较AUC，临床决策曲线评估净收益。

结果

ChatGPT-4V诊断效能接近低年资医师（准确度

0.05），但低于高年资医师（

0.05），临床决策曲线显示低阈值净收益接近低年资医师。ChatGPT-4V与低年资医师比较，在回声类型（

=0.012）、后方回声（

=0.018）方面的识别准确度显著更低，钙化特征识别差异无统计学意义（

=1.000）；ChatGPT-4V与高年资医师比较，在形状、边界、回声类型、后方回声及钙化所有超声特征的识别上均显著不足（

0.05）。ChatGPT-4V误判24例（16.0%），恶性误为良性多见于边界光整，良性误为恶性多见于不规则形状。

结论

ChatGPT-4V接近低年资医师效能，适合基层筛查辅助，但在复杂特征识别方面需改进，未来可继续优化以提升临床应用价值。

Abstract

Objective

To evaluate the diagnostic performance and feature recognition accuracy of ChatGPT-4V in classifying benign and malignant breast lesions on ultrasound

compared with junior and senior physicians

and to explore its feasibility as an auxiliary diagnostic tool.

Methods

A retrospective study included patients with breast lesions from The First Hospital of Qinhuangdao between January 2024 and June 2025. With pathological examination results as the gold standard

ChatGPT-4V

two junior physicians (3-5 years of experience)

and two senior physicians (>10 years of experience) independently interpreted ultrasound images in a blinded manner. Sensitivity

specificity

accuracy

and area under the receiver operating characteristic curve (AUC) were recorded. The accuracy of identifying ultrasound features

including shape

margin

echo pattern

posterior acoustic features

and calcifications

were evaluated against the criteria of the Breast Imaging Reporting and Data System (BI-RADS). The McNemar test was used to compare diagnostic accuracy among different interpreters

the DeLong method was employed to compare AUC values

and decision curve analysis (DCA) was performed to assess the net benefit across varying threshold probabilities.

Results

ChatGPT-4V’s diagnostic performance was comparable to that of junior physicians (accuracy

0.05) but inferior to senior physicians (

0.05). DCA showed similar net benefit to junior physicians at low thresholds. Compared with junior physicians

ChatGPT-4V had significantly lower accuracy in identifying echo pattern (

=0.012) and posterior features (

=0.018)

with no statistical difference in calcification recognition (

=1.000). Compared with senior physicians

ChatGPT-4V showed significantly insufficient accuracy in recognizing all ultrasound features

including shape

margin

echo pattern

posterior features

and calcification (

0.05). ChatGPT-4V misdiagnosed 24 cases (16.0%)

with malignant-to-benign errors often linked to smooth margins and benign-to-malignant errors to irregular shapes.

Conclusion

ChatGPT-4V demonstrates diagnostic performance close to junior physicians

making it a potential auxiliary tool for breast ultrasound screening in primary care. However

its limitations in complex feature recognition require improvement through targeted optimization to enhance clinical utility.

关键词

Keywords

references

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