Exploring the integration of artificial intelligence in radiology education: A scoping review.

Muying Lucy Hui, Ethan Sacoransky, Andrew Chung, Benjamin YM Kwan
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Abstract

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.

探索将人工智能融入放射学教育:范围综述。
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。
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