探索将人工智能融入放射学教育:范围综述。

Muying Lucy Hui, Ethan Sacoransky, Andrew Chung, Benjamin YM Kwan
{"title":"探索将人工智能融入放射学教育:范围综述。","authors":"Muying Lucy Hui, Ethan Sacoransky, Andrew Chung, Benjamin YM Kwan","doi":"10.1067/j.cpradiol.2024.10.012","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>The integration of Artificial Intelligence (AI) into radiology education presents a transformative opportunity to enhance learning and practice within the field. This scoping review aims to systematically explore and map the current landscape of AI integration in radiology education.</p><p><strong>Methods: </strong>The review process involved systematically searching four databases, including MEDLINE (Ovid), Embase (Ovid), PsychINFO (Ovid), and Scopus. Inclusion criteria focused on research that addresses the use of AI technologies in radiology education, including but not limited to, AI-assisted learning platforms, simulation tools, and automated assessment systems. This scoping review was registered on Open Science Framework using the Preferred Reporting Items for Systematic reviews and Meta-Analysis (PRISMA) extension to scoping review.</p><p><strong>Results: </strong>Of the 1081 search results, 9 studies met the inclusion criteria. Key findings indicate a diverse range of AI applications in radiology education, from personalized curriculum generation and diagnostic support tools to automated evaluation systems. The review highlights both the potential benefits, such as enhanced diagnostic accuracy, and the challenges, including technical limitations.</p><p><strong>Conclusion: </strong>The integration of AI into radiology education, which has significant potential to enhance outcomes and professional practice, requires overcoming existing challenges and ensuring that AI complements rather than replaces traditional methods, with future research needed on longitudinal studies to evaluate its long-term impact.</p>","PeriodicalId":93969,"journal":{"name":"Current problems in diagnostic radiology","volume":" ","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Exploring the integration of artificial intelligence in radiology education: A scoping review.\",\"authors\":\"Muying Lucy Hui, Ethan Sacoransky, Andrew Chung, Benjamin YM Kwan\",\"doi\":\"10.1067/j.cpradiol.2024.10.012\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>The integration of Artificial Intelligence (AI) into radiology education presents a transformative opportunity to enhance learning and practice within the field. This scoping review aims to systematically explore and map the current landscape of AI integration in radiology education.</p><p><strong>Methods: </strong>The review process involved systematically searching four databases, including MEDLINE (Ovid), Embase (Ovid), PsychINFO (Ovid), and Scopus. Inclusion criteria focused on research that addresses the use of AI technologies in radiology education, including but not limited to, AI-assisted learning platforms, simulation tools, and automated assessment systems. This scoping review was registered on Open Science Framework using the Preferred Reporting Items for Systematic reviews and Meta-Analysis (PRISMA) extension to scoping review.</p><p><strong>Results: </strong>Of the 1081 search results, 9 studies met the inclusion criteria. Key findings indicate a diverse range of AI applications in radiology education, from personalized curriculum generation and diagnostic support tools to automated evaluation systems. The review highlights both the potential benefits, such as enhanced diagnostic accuracy, and the challenges, including technical limitations.</p><p><strong>Conclusion: </strong>The integration of AI into radiology education, which has significant potential to enhance outcomes and professional practice, requires overcoming existing challenges and ensuring that AI complements rather than replaces traditional methods, with future research needed on longitudinal studies to evaluate its long-term impact.</p>\",\"PeriodicalId\":93969,\"journal\":{\"name\":\"Current problems in diagnostic radiology\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-10-05\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Current problems in diagnostic radiology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1067/j.cpradiol.2024.10.012\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Current problems in diagnostic radiology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1067/j.cpradiol.2024.10.012","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

背景:将人工智能(AI)融入放射学教育为提高该领域的学习和实践水平提供了一个变革性的机会。本范围综述旨在系统地探索和描绘目前放射学教育中人工智能整合的现状:综述过程包括系统检索四个数据库,包括MEDLINE(Ovid)、Embase(Ovid)、PsychINFO(Ovid)和Scopus。纳入标准主要针对在放射学教育中使用人工智能技术的研究,包括但不限于人工智能辅助学习平台、模拟工具和自动评估系统。本范围界定综述采用系统综述和元分析首选报告项目(PRISMA)扩展到范围界定综述,并在开放科学框架上进行了注册:在 1081 项搜索结果中,有 9 项研究符合纳入标准。主要研究结果表明,从个性化课程生成、诊断支持工具到自动评估系统,人工智能在放射学教育中的应用多种多样。综述既强调了潜在的益处,如提高诊断准确性,也强调了挑战,包括技术限制:将人工智能融入放射学教育,具有提高成果和专业实践的巨大潜力,需要克服现有挑战,确保人工智能补充而非取代传统方法,未来需要开展纵向研究,以评估其长期影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Exploring the integration of artificial intelligence in radiology education: A scoping review.

Background: The integration of Artificial Intelligence (AI) into radiology education presents a transformative opportunity to enhance learning and practice within the field. This scoping review aims to systematically explore and map the current landscape of AI integration in radiology education.

Methods: The review process involved systematically searching four databases, including MEDLINE (Ovid), Embase (Ovid), PsychINFO (Ovid), and Scopus. Inclusion criteria focused on research that addresses the use of AI technologies in radiology education, including but not limited to, AI-assisted learning platforms, simulation tools, and automated assessment systems. This scoping review was registered on Open Science Framework using the Preferred Reporting Items for Systematic reviews and Meta-Analysis (PRISMA) extension to scoping review.

Results: Of the 1081 search results, 9 studies met the inclusion criteria. Key findings indicate a diverse range of AI applications in radiology education, from personalized curriculum generation and diagnostic support tools to automated evaluation systems. The review highlights both the potential benefits, such as enhanced diagnostic accuracy, and the challenges, including technical limitations.

Conclusion: The integration of AI into radiology education, which has significant potential to enhance outcomes and professional practice, requires overcoming existing challenges and ensuring that AI complements rather than replaces traditional methods, with future research needed on longitudinal studies to evaluate its long-term impact.

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
自引率
0.00%
发文量
0
×
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
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
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 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信