[Evaluating the accuracy of large language models in answering mammography screening questions in Italian and English: a study based on the Eusobi guidelines.]

乳腺摄影术 乳腺X光筛查 计算机科学 答疑 自然语言处理 医学物理学 情报检索 医学 内科学 癌症 乳腺癌
作者
M. Signorini,Silvia Fontani,Paola Minichetti,Silvia Teggi,Alessandra Barusco,Massimo Favat
出处
期刊:PubMed 卷期号:116 (3): 162-167
标识
DOI:10.1701/4460.44556
摘要

Artificial intelligence (AI) is transforming various aspects of everyday life, including healthcare, through large language models (LLMs) like ChatGPT, Gemini, and Copilot. These systems are increasingly used to disseminate medical information, allowing patients to access simplified explanations. This study aims to compare responses to breast imaging-related questions formulated in Italian and English, based on Eusobi guidelines, evaluating the LLMs' ability to provide accurate and complete answers on mammography screening concepts. Nine questions related to breast cancer screening were developed by five breast radiologists based on Eusobi recommendations. These questions were submitted to ChatGPT, Gemini, and Copilot in both Italian and English. Responses were evaluated by two expert breast radiologists using a Likert scale (1 to 5), with statistical analysis performed to compare the accuracy, average length of responses, use of radiological sources and the agreement among readers. The average scores for responses were similar in both languages, ranging from 3.6 to 4 out of 5. Questions on general mammography concepts received more accurate answers, while more specific questions based on the latest guidelines showed incomplete responses, especially about the definition of dense breast. The sources used, particularly in Italian, were often non-specialized in radiology, highlighting a limitation of LLMs in providing detailed and up-to-date medical answers. The study shows that LLMs are useful tools for medical communication, but they have limitations in delivering accurate answers on highly specialized medical topics. To improve the quality of information, collaboration between AI experts and healthcare professionals is necessary, especially in breast cancer prevention and screening.

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
刚刚
ding应助阳娅丽采纳,获得10
1秒前
CFD应助小陈同学采纳,获得10
1秒前
义气夜云完成签到,获得积分20
1秒前
画出发布了新的文献求助30
1秒前
jingluo完成签到,获得积分10
2秒前
2秒前
乐邦詹士完成签到,获得积分10
2秒前
2秒前
稳赚赚完成签到,获得积分0
2秒前
充电宝应助朱良宇采纳,获得10
2秒前
李程阳完成签到,获得积分10
2秒前
2秒前
年123完成签到 ,获得积分10
3秒前
4秒前
流沙无言发布了新的文献求助10
4秒前
tulipqq完成签到 ,获得积分10
4秒前
wanglejia完成签到,获得积分10
4秒前
芋泥丸丸完成签到,获得积分10
4秒前
wyyj发布了新的文献求助10
4秒前
含蓄觅山发布了新的文献求助10
5秒前
5秒前
viper完成签到,获得积分10
5秒前
帅帅哈完成签到,获得积分10
6秒前
大模型应助坚强的晓博采纳,获得10
6秒前
木卡卡完成签到,获得积分10
6秒前
maaicui完成签到,获得积分10
7秒前
7秒前
余杭村王小虎完成签到,获得积分10
7秒前
fsznc1完成签到 ,获得积分0
7秒前
7秒前
jingluo发布了新的文献求助10
8秒前
忧伤的老四完成签到,获得积分10
8秒前
8秒前
jovial完成签到,获得积分20
8秒前
冷静勒完成签到,获得积分20
9秒前
9秒前
一一完成签到,获得积分10
9秒前
great7701完成签到,获得积分10
9秒前
木樨完成签到,获得积分10
9秒前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
Prompt Engineering for Clinicians: Harnessing AI in Everyday Medical Practice 600
University Physics for the Life Sciences 500
REAL-WORLD EFFICACY AND GENOMIC LANDSCAPE OF POLATUZUMA VEDOTIN-BASED FIRST-LINE THERAPY IN DIFFUSE LARGE B-CELL LYMPHOMA: A FOCUS ON TP53 MUTATIONS AND TREATMENT RESPONSE 500
Handbook of Luminescence Dating 500
Safety Pharmacology 500
《KNN基无铅压电陶瓷电学性能优化与物理机理研究》 500
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 工程类 有机化学 计算机科学 化学工程 生物化学 物理 内科学 复合材料 催化作用 光电子学 物理化学 电极 细胞生物学 基因 遗传学
热门帖子
关注 科研通微信公众号,转发送积分 6952376
求助须知:如何正确求助?哪些是违规求助? 8636496
关于积分的说明 18313374
捐赠科研通 6395423
什么是DOI,文献DOI怎么找? 3082384
关于科研通互助平台的介绍 2127942
邀请新用户注册赠送积分活动 2059258