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儿科放射科住院医师培训项目的人工智能课程需求评估:内容、方式及原因?

Artificial Intelligence Curriculum Needs Assessment for a Pediatric Radiology Fellowship Program: What, How, and Why?

作者信息

Velez-Florez Maria Camila, Ghosh Adarsh, Patton Daniela, Sze Raymond, Reid Janet R, Sotardi Susan

机构信息

Children's Hospital of Philadelphia, Department of Radiology, 3401 Civic Center Blvd., Philadelphia, PA. 19104.

Children's Hospital of Philadelphia, Department of Radiology, 3401 Civic Center Blvd., Philadelphia, PA. 19104.

出版信息

Acad Radiol. 2023 Feb;30(2):349-358. doi: 10.1016/j.acra.2022.04.026. Epub 2022 Jun 23.

Abstract

RATIONALE AND OBJECTIVES

Artificial intelligence (AI) holds enormous potential for improvements in patient care, efficiency, and innovation in pediatric radiology practice. Although there is a pressing need for a radiology-specific training curriculum and formalized AI teaching, few resources are available. The purpose of our study was to perform a needs assessment for the development of an AI curriculum during pediatric radiology training and continuing education.

MATERIALS AND METHODS

A focus group study using a semistructured moderator-guided interview was conducted with radiology trainees' and attending radiologists' perceptions of AI, perceived competence in interpretation of AI literature, and perceived expectations from radiology AI educational programs. The focus group was audio-recorded, transcribed, and thematic analysis was performed.

RESULTS

The focus group was held virtually with seven participants. The following themes we identified: (1) AI knowledge, (2) previous training, (3) learning preferences, (4) AI expectations, and (5) AI concerns. The participants had no previous formal training in AI and variability in perceived needs and interests. Most preferred a case-based approach to teaching AI. They expressed incomplete understanding of AI hindered its clinical applicability and reiterated a need for improved training in the interpretation and application of AI literature in their practice.

CONCLUSION

We found heterogeneity in perspectives about AI; thus, a curriculum must account for the wide range of these interests and needs. Teaching the interpretation of AI research methods, literature critique, and quality control through implementation of specific scenarios could engage a variety of trainees from different backgrounds and interest levels while ensuring a baseline level of competency in AI.

摘要

原理与目标

人工智能在改善儿科放射学实践中的患者护理、效率和创新方面具有巨大潜力。尽管迫切需要针对放射学的培训课程和正式的人工智能教学,但可用资源却很少。我们研究的目的是在儿科放射学培训和继续教育期间对人工智能课程的开发进行需求评估。

材料与方法

采用半结构化主持人引导访谈的焦点小组研究,了解放射学实习生和主治放射科医生对人工智能的看法、对人工智能文献解读的感知能力以及对放射学人工智能教育项目的期望。焦点小组进行了录音、转录并进行了主题分析。

结果

焦点小组以线上方式进行,有七名参与者。我们确定了以下主题:(1)人工智能知识,(2)先前培训,(3)学习偏好,(4)人工智能期望,以及(5)对人工智能的担忧。参与者此前没有接受过人工智能方面的正式培训,在感知需求和兴趣方面存在差异。大多数人更喜欢基于案例的人工智能教学方法。他们表示对人工智能的理解不完整阻碍了其临床应用,并再次强调需要在实践中加强对人工智能文献解读和应用的培训。

结论

我们发现对人工智能的看法存在异质性;因此,课程必须考虑到这些广泛的兴趣和需求。通过实施特定场景来教授人工智能研究方法的解读、文献评论和质量控制,可以吸引来自不同背景和兴趣水平的各类学员,同时确保人工智能方面的基本能力水平。

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