中国放射科的职业倦怠危机:人工智能会有所帮助吗?

IF 4.7 2区 医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING European Radiology Pub Date : 2024-11-20 DOI:10.1007/s00330-024-11206-4
Xiao Fang, Can Ma, Xia Liu, Xiaofeng Deng, Jianhui Liao, Tianyang Zhang
{"title":"中国放射科的职业倦怠危机:人工智能会有所帮助吗?","authors":"Xiao Fang, Can Ma, Xia Liu, Xiaofeng Deng, Jianhui Liao, Tianyang Zhang","doi":"10.1007/s00330-024-11206-4","DOIUrl":null,"url":null,"abstract":"<p><strong>Objectives: </strong>To assess the correlation between the use of artificial intelligence (AI) software and burnout in the radiology departments of hospitals in China.</p><p><strong>Methods: </strong>This study employed a cross-sectional research design. From February to July 2024, an online survey was conducted among radiologists and technicians at 68 public hospitals in China. The survey utilized general information questionnaires, the Maslach Burnout Inventory-Human Services Survey (MBI-HSS) scale, and a custom-designed AI usage questionnaire. This study analyzed the correlation between AI software usage and occupational burnout, and general information was included as a control variable in a multiple linear regression analysis.</p><p><strong>Results: </strong>The analysis of survey data from 522 radiology staff revealed that 389 (74.5%) had used AI and that 252 (48.3%) had used it for more than 12 months. Only 133 (25.5%) had not yet adopted AI. Among the respondents, radiologists had a higher AI usage rate (82.0%) than technicians (only 59.9%). Furthermore, 344 (65.9%) of the respondents exhibited signs of burnout. The duration of AI software usage was significantly negatively correlated with overall burnout, yielding a Pearson correlation coefficient of -0.112 (p < 0.05). Multiple stepwise regression analysis revealed that salary satisfaction, night shifts, duration of AI usage, weekly working hours, having children, and professional rank were the main factors influencing occupational burnout (all p < 0.05).</p><p><strong>Conclusion: </strong>AI has the potential to significantly help mitigate occupational burnout among radiology staff. This study reveals the key role that AI plays in assisting radiology staff in their work.</p><p><strong>Key points: </strong>Questions Although we are aware that radiology staff burnout is intensifying, there is no quantitative research assessing whether artificial intelligence software can mitigate this occupational burnout. Findings The longer staff use deep learning-based artificial intelligence imaging software, the less severe their occupational burnout tends to be. This result is particularly evident among radiologists. Clinical relevance In China, radiologists and technicians experience high burnout rates. Even if there is an artificial intelligence usage controversy, encouraging the use of artificial intelligence software in radiology helps prevent and alleviate this occupational burnout.</p>","PeriodicalId":12076,"journal":{"name":"European Radiology","volume":" ","pages":""},"PeriodicalIF":4.7000,"publicationDate":"2024-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Burnout crisis in Chinese radiology: will artificial intelligence help?\",\"authors\":\"Xiao Fang, Can Ma, Xia Liu, Xiaofeng Deng, Jianhui Liao, Tianyang Zhang\",\"doi\":\"10.1007/s00330-024-11206-4\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Objectives: </strong>To assess the correlation between the use of artificial intelligence (AI) software and burnout in the radiology departments of hospitals in China.</p><p><strong>Methods: </strong>This study employed a cross-sectional research design. From February to July 2024, an online survey was conducted among radiologists and technicians at 68 public hospitals in China. The survey utilized general information questionnaires, the Maslach Burnout Inventory-Human Services Survey (MBI-HSS) scale, and a custom-designed AI usage questionnaire. This study analyzed the correlation between AI software usage and occupational burnout, and general information was included as a control variable in a multiple linear regression analysis.</p><p><strong>Results: </strong>The analysis of survey data from 522 radiology staff revealed that 389 (74.5%) had used AI and that 252 (48.3%) had used it for more than 12 months. Only 133 (25.5%) had not yet adopted AI. Among the respondents, radiologists had a higher AI usage rate (82.0%) than technicians (only 59.9%). Furthermore, 344 (65.9%) of the respondents exhibited signs of burnout. The duration of AI software usage was significantly negatively correlated with overall burnout, yielding a Pearson correlation coefficient of -0.112 (p < 0.05). Multiple stepwise regression analysis revealed that salary satisfaction, night shifts, duration of AI usage, weekly working hours, having children, and professional rank were the main factors influencing occupational burnout (all p < 0.05).</p><p><strong>Conclusion: </strong>AI has the potential to significantly help mitigate occupational burnout among radiology staff. This study reveals the key role that AI plays in assisting radiology staff in their work.</p><p><strong>Key points: </strong>Questions Although we are aware that radiology staff burnout is intensifying, there is no quantitative research assessing whether artificial intelligence software can mitigate this occupational burnout. Findings The longer staff use deep learning-based artificial intelligence imaging software, the less severe their occupational burnout tends to be. This result is particularly evident among radiologists. Clinical relevance In China, radiologists and technicians experience high burnout rates. Even if there is an artificial intelligence usage controversy, encouraging the use of artificial intelligence software in radiology helps prevent and alleviate this occupational burnout.</p>\",\"PeriodicalId\":12076,\"journal\":{\"name\":\"European Radiology\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":4.7000,\"publicationDate\":\"2024-11-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"European Radiology\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1007/s00330-024-11206-4\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"European Radiology","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1007/s00330-024-11206-4","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING","Score":null,"Total":0}
引用次数: 0

摘要

目的:评估中国医院放射科使用人工智能(AI)软件与职业倦怠之间的相关性:评估中国医院放射科使用人工智能(AI)软件与职业倦怠之间的相关性:本研究采用横断面研究设计。从 2024 年 2 月至 7 月,对中国 68 家公立医院的放射科医生和技术人员进行了在线调查。调查采用了一般信息问卷、马斯拉克职业倦怠量表--人类服务调查(MBI-HSS)量表和定制的人工智能使用情况问卷。本研究分析了人工智能软件使用与职业倦怠之间的相关性,并将一般信息作为控制变量纳入多元线性回归分析:对 522 名放射科工作人员的调查数据进行分析后发现,389 人(74.5%)使用过人工智能,其中 252 人(48.3%)使用时间超过 12 个月。只有 133 人(25.5%)尚未采用人工智能。在受访者中,放射科医生的人工智能使用率(82.0%)高于技术人员(仅 59.9%)。此外,344 名受访者(65.9%)表现出职业倦怠迹象。使用人工智能软件的持续时间与总体倦怠感呈显著负相关,皮尔逊相关系数为-0.112(p 结论:人工智能有可能极大地帮助提高工作效率:人工智能有可能大大有助于减轻放射科工作人员的职业倦怠。本研究揭示了人工智能在协助放射科工作人员工作方面发挥的关键作用:问题 尽管我们意识到放射科工作人员的职业倦怠正在加剧,但目前还没有量化研究来评估人工智能软件能否减轻这种职业倦怠。研究结果 员工使用基于深度学习的人工智能成像软件的时间越长,其职业倦怠的程度往往越轻。这一结果在放射科医生中尤为明显。临床意义 在中国,放射科医生和技术人员的职业倦怠率很高。即使人工智能的使用存在争议,鼓励放射科使用人工智能软件也有助于预防和缓解这种职业倦怠。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Burnout crisis in Chinese radiology: will artificial intelligence help?

Objectives: To assess the correlation between the use of artificial intelligence (AI) software and burnout in the radiology departments of hospitals in China.

Methods: This study employed a cross-sectional research design. From February to July 2024, an online survey was conducted among radiologists and technicians at 68 public hospitals in China. The survey utilized general information questionnaires, the Maslach Burnout Inventory-Human Services Survey (MBI-HSS) scale, and a custom-designed AI usage questionnaire. This study analyzed the correlation between AI software usage and occupational burnout, and general information was included as a control variable in a multiple linear regression analysis.

Results: The analysis of survey data from 522 radiology staff revealed that 389 (74.5%) had used AI and that 252 (48.3%) had used it for more than 12 months. Only 133 (25.5%) had not yet adopted AI. Among the respondents, radiologists had a higher AI usage rate (82.0%) than technicians (only 59.9%). Furthermore, 344 (65.9%) of the respondents exhibited signs of burnout. The duration of AI software usage was significantly negatively correlated with overall burnout, yielding a Pearson correlation coefficient of -0.112 (p < 0.05). Multiple stepwise regression analysis revealed that salary satisfaction, night shifts, duration of AI usage, weekly working hours, having children, and professional rank were the main factors influencing occupational burnout (all p < 0.05).

Conclusion: AI has the potential to significantly help mitigate occupational burnout among radiology staff. This study reveals the key role that AI plays in assisting radiology staff in their work.

Key points: Questions Although we are aware that radiology staff burnout is intensifying, there is no quantitative research assessing whether artificial intelligence software can mitigate this occupational burnout. Findings The longer staff use deep learning-based artificial intelligence imaging software, the less severe their occupational burnout tends to be. This result is particularly evident among radiologists. Clinical relevance In China, radiologists and technicians experience high burnout rates. Even if there is an artificial intelligence usage controversy, encouraging the use of artificial intelligence software in radiology helps prevent and alleviate this occupational burnout.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
European Radiology
European Radiology 医学-核医学
CiteScore
11.60
自引率
8.50%
发文量
874
审稿时长
2-4 weeks
期刊介绍: European Radiology (ER) continuously updates scientific knowledge in radiology by publication of strong original articles and state-of-the-art reviews written by leading radiologists. A well balanced combination of review articles, original papers, short communications from European radiological congresses and information on society matters makes ER an indispensable source for current information in this field. This is the Journal of the European Society of Radiology, and the official journal of a number of societies. From 2004-2008 supplements to European Radiology were published under its companion, European Radiology Supplements, ISSN 1613-3749.
期刊最新文献
Correction: Comparison between CT volumetry and extracellular volume fraction using liver dynamic CT for the predictive ability of liver fibrosis in patients with hepatocellular carcinoma. Correction: Development and evaluation of two open-source nnU-Net models for automatic segmentation of lung tumors on PET and CT images with and without respiratory motion compensation. Correction: Machine learning detects symptomatic patients with carotid plaques based on 6-type calcium configuration classification on CT angiography. Natural language processing pipeline to extract prostate cancer-related information from clinical notes. ESR Essentials: characterisation and staging of adnexal masses with MRI and CT-practice recommendations by ESUR.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1