Probing clarity: AI-generated simplified breast imaging reports for enhanced patient comprehension powered by ChatGPT-4o.

IF 3.7 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
Roberto Maroncelli, Veronica Rizzo, Marcella Pasculli, Federica Cicciarelli, Massimo Macera, Francesca Galati, Carlo Catalano, Federica Pediconi
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引用次数: 0

Abstract

Background: To assess the reliability and comprehensibility of breast radiology reports simplified by artificial intelligence using the large language model (LLM) ChatGPT-4o.

Methods: 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.

Results: 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.

Conclusion: 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.

Relevance statement: 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.

Key points: 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.

探查清晰:由 ChatGPT-4o 支持的人工智能生成的简化乳腺成像报告可提高患者的理解能力。
背景:评估人工智能简化乳腺放射学报告的可靠性和可理解性:评估人工智能使用大型语言模型(LLM)ChatGPT-4o简化的乳腺放射学报告的可靠性和可理解性:一位有 20 年经验的放射科医生选择了 21 份匿名的乳腺放射学报告,其中 7 份是乳腺 X 线照相术,7 份是乳腺超声波检查,7 份是乳腺磁共振成像(MRI),并根据乳腺成像报告和数据系统(BI-RADS)进行了分类。在 ChatGPT-4o 中提示 "用简单的语言向患者解释这份医疗报告",从而简化了这些报告。五位乳腺放射科医生用 1 分(非常同意)到 5 分(非常不同意)的 5 点李克特量表评估了这些简化报告在事实准确性、完整性和潜在危害方面的质量。另一名乳腺放射科医生采用 1 分(优秀)到 5 分(较差)的 5 级李克特量表对五名非医护人员读者的文字理解能力进行了评估。使用了描述性统计、Cronbach's α 和 Kruskal-Wallis 检验:结果:不同放射科医生的乳腺 X 射线照相术、超声波检查和核磁共振成像显示出较高的事实准确性(中位数为 2)和完整性(中位数为 2),潜在危害得分较低(中位数为 5);没有观察到显著的组间差异(p ≥ 0.780)和较高的内部一致性(α > 0.80)。非医疗保健读者的理解能力较高(乳腺 X 射线照相术和核磁共振成像的中位数为 2,超声波为 1);不同模式之间无明显组间差异(p = 0.368),内部一致性较高(α > 0.85)。BI-RADS0、1和2报告得到了准确的解释,而BI-RADS3-6报告则具有挑战性:结论:该模型显示了可靠性和清晰度,为不同背景的患者提供了希望。像 ChatGPT-4o 这样的 LLM 可以简化乳腺放射学报告、帮助沟通并加强患者护理:由 ChatGPT-4o 生成的简化乳腺放射学报告在加强与患者的沟通、提高不同教育背景的患者的理解能力以及在放射学实践中促进以患者为中心的护理方面显示出潜力:人工智能简化了复杂的乳腺成像报告,提高了患者的理解能力。人工智能简化的报告保持了准确性,大大提高了患者的理解能力。实施人工智能报告可提高患者对乳腺成像的参与度和沟通能力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
European Radiology Experimental
European Radiology Experimental Medicine-Radiology, Nuclear Medicine and Imaging
CiteScore
6.70
自引率
2.60%
发文量
56
审稿时长
18 weeks
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