What makes a good scientific presentation on artificial intelligence in medical imaging?

IF 1.8 4区 医学 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING Clinical Imaging Pub Date : 2024-06-01 DOI:10.1016/j.clinimag.2024.110212
Stefan J. Fransen, Quintin van Lohuizen, Christian Roest, Derya Yakar, Thomas C. Kwee
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Abstract

Purpose

Adequate communication of scientific findings is crucial to enhance knowledge transfer. This study aimed to determine the key features of a good scientific oral presentation on artificial intelligence (AI) in medical imaging.

Methods

A total of 26 oral presentations dealing with original research on AI studies in medical imaging at the 2023 RSNA annual meeting were included and systematically assessed by three observers. The presentation quality of the research question, inclusion criteria, reference standard, method, results, clinical impact, presentation clarity, presenter engagement, and the presentation's quality of knowledge transfer were assessed using five-point Likert scales. The number of slides, the average number of words per slide, the number of interactive slides, the number of figures, and the number of tables were also determined for each presentation. Mixed-effects ordinal regression was used to assess the association between the above-mentioned variables and the quality of knowledge transfer of the presentation.

Results

A significant positive association was found between the quality of the presentation of the research question and the presentation's quality of knowledge transfer (odds ratio [OR]: 2.5, P = 0.005). The average number of words per slide was significantly negatively associated with the presentation's quality of knowledge transfer (OR: 0.9, P < 0.001). No other significant associations were found.

Conclusion

Researchers who orally present their scientific findings in the field of AI and medical imaging should pay attention to clearly communicating their research question and minimizing the number of words per slide to maximize the value of their presentation.

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怎样才能做好医学影像人工智能的科学报告?
目的 科学研究成果的充分交流对于促进知识转移至关重要。本研究旨在确定医学影像领域人工智能(AI)科学口头报告的关键特征。方法在 2023 年 RSNA 年会上,共有 26 篇涉及医学影像领域人工智能研究原创性研究的口头报告被纳入其中,并由三名观察员进行系统评估。采用五点李克特量表对研究问题、纳入标准、参考标准、方法、结果、临床影响、演讲清晰度、演讲者参与度以及演讲的知识传递质量进行评估。此外,还确定了每个演示文稿的幻灯片数量、每张幻灯片的平均字数、互动幻灯片数量、图表数量和表格数量。混合效应序数回归用于评估上述变量与演示文稿的知识传递质量之间的关系。结果发现,研究问题的演示文稿质量与演示文稿的知识传递质量之间存在显著的正相关关系(几率比 [OR]:2.5,P = 0.005)。每张幻灯片的平均字数与演示文稿的知识传递质量呈显著负相关(OR:0.9,P = 0.001)。结论人工智能和医学影像领域的研究人员在口头报告他们的科研成果时,应注意清楚地表达他们的研究问题,并尽量减少每张幻灯片的字数,以最大限度地提高报告的价值。
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来源期刊
Clinical Imaging
Clinical Imaging 医学-核医学
CiteScore
4.60
自引率
0.00%
发文量
265
审稿时长
35 days
期刊介绍: The mission of Clinical Imaging is to publish, in a timely manner, the very best radiology research from the United States and around the world with special attention to the impact of medical imaging on patient care. The journal''s publications cover all imaging modalities, radiology issues related to patients, policy and practice improvements, and clinically-oriented imaging physics and informatics. The journal is a valuable resource for practicing radiologists, radiologists-in-training and other clinicians with an interest in imaging. Papers are carefully peer-reviewed and selected by our experienced subject editors who are leading experts spanning the range of imaging sub-specialties, which include: -Body Imaging- Breast Imaging- Cardiothoracic Imaging- Imaging Physics and Informatics- Molecular Imaging and Nuclear Medicine- Musculoskeletal and Emergency Imaging- Neuroradiology- Practice, Policy & Education- Pediatric Imaging- Vascular and Interventional Radiology
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