Department of Radiological, Oncological and Pathological Sciences, Sapienza-University of Rome, Rome, Roma, Italy.
Federico II-University of Naples, Naples, Italy.
Eur Radiol Exp. 2024 Oct 30;8(1):124. doi: 10.1186/s41747-024-00526-1.
To assess the reliability and comprehensibility of breast radiology reports simplified by artificial intelligence using the large language model (LLM) ChatGPT-4o.
A radiologist with 20 years' experience selected 21 anonymized breast radiology reports, 7 mammography, 7 breast ultrasound, and 7 breast magnetic resonance imaging (MRI), categorized according to breast imaging reporting and data system (BI-RADS). These reports underwent simplification by prompting ChatGPT-4o with "Explain this medical report to a patient using simple language". Five breast radiologists assessed the quality of these simplified reports for factual accuracy, completeness, and potential harm with a 5-point Likert scale from 1 (strongly agree) to 5 (strongly disagree). Another breast radiologist evaluated the text comprehension of five non-healthcare personnel readers using a 5-point Likert scale from 1 (excellent) to 5 (poor). Descriptive statistics, Cronbach's α, and the Kruskal-Wallis test were used.
Mammography, ultrasound, and MRI showed high factual accuracy (median 2) and completeness (median 2) across radiologists, with low potential harm scores (median 5); no significant group differences (p ≥ 0.780), and high internal consistency (α > 0.80) were observed. Non-healthcare readers showed high comprehension (median 2 for mammography and MRI and 1 for ultrasound); no significant group differences across modalities (p = 0.368), and high internal consistency (α > 0.85) were observed. BI-RADS 0, 1, and 2 reports were accurately explained, while BI-RADS 3-6 reports were challenging.
The model demonstrated reliability and clarity, offering promise for patients with diverse backgrounds. LLMs like ChatGPT-4o could simplify breast radiology reports, aid in communication, and enhance patient care.
Simplified breast radiology reports generated by ChatGPT-4o show potential in enhancing communication with patients, improving comprehension across varying educational backgrounds, and contributing to patient-centered care in radiology practice.
AI simplifies complex breast imaging reports, enhancing patient understanding. Simplified reports from AI maintain accuracy, improving patient comprehension significantly. Implementing AI reports enhances patient engagement and communication in breast imaging.
使用大型语言模型（LLM）ChatGPT-4o 评估人工智能简化的乳腺放射学报告的可靠性和可理解性。
一位具有 20 年经验的放射科医生选择了 21 份匿名乳腺放射学报告，7 份乳腺 X 线摄影、7 份乳腺超声和 7 份乳腺磁共振成像（MRI），根据乳腺成像报告和数据系统（BI-RADS）进行分类。这些报告通过向 ChatGPT-4o 提示“用简单的语言向患者解释这份医学报告”进行简化。五名乳腺放射科医生使用 5 分李克特量表（从 1 分（非常同意）到 5 分（非常不同意））评估这些简化报告的质量，以确定其事实准确性、完整性和潜在危害。另一名乳腺放射科医生使用 5 分李克特量表（从 1 分（优秀）到 5 分（差））评估五名非医疗保健人员读者的文本理解能力。使用描述性统计、克朗巴赫 α 和克鲁斯卡尔-沃利斯检验进行分析。
在放射科医生中，乳腺 X 线摄影、超声和 MRI 的事实准确性（中位数 2）和完整性（中位数 2）较高，潜在危害评分较低（中位数 5）；组间无显著差异（p≥0.780），内部一致性高（α>0.80）。非医疗保健读者的理解能力较高（乳腺 X 线摄影和 MRI 的中位数为 2，超声的中位数为 1）；各模式间无显著组间差异（p=0.368），内部一致性高（α>0.85）。BI-RADS 0、1 和 2 报告解释准确，而 BI-RADS 3-6 报告具有挑战性。
该模型具有可靠性和清晰度，为背景各异的患者带来了希望。像 ChatGPT-4o 这样的 LLM 可以简化乳腺放射学报告，有助于交流，并改善放射科的患者护理。
ChatGPT-4o 生成的简化乳腺放射学报告具有增强与患者沟通的潜力，提高不同教育背景患者的理解能力，并为放射科实践中的以患者为中心的护理做出贡献。
人工智能简化复杂的乳腺影像学报告，增强患者理解。人工智能生成的简化报告保持准确性，显著提高患者的理解能力。实施人工智能报告可增强患者在乳腺成像中的参与度和沟通能力。
