Enhancing puncture skills training with generative AI and digital technologies: a parallel cohort study.

IF 2.7 2区 医学 Q1 EDUCATION & EDUCATIONAL RESEARCH BMC Medical Education Pub Date : 2024-11-19 DOI:10.1186/s12909-024-06217-0
Zhe Ji, Yuliang Jiang, Haitao Sun, Bin Qiu, Yi Chen, Mao Li, Jinghong Fan, Junjie Wang
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Abstract

Background: Traditional puncture skills training for refresher doctors faces limitations in effectiveness and efficiency. This study explored the application of generative AI (ChatGPT), templates, and digital imaging to enhance puncture skills training.

Methods: 90 refresher doctors were enrolled sequentially into 3 groups: traditional training; template and digital imaging training; and ChatGPT, template and digital imaging training. Outcomes included theoretical knowledge, technical skills, and trainee satisfaction measured at baseline, post-training, and 3-month follow-up.

Results: The ChatGPT group increased theoretical knowledge scores by 17-21% over traditional training at post-training (81.6 ± 4.56 vs. 69.6 ± 4.58, p < 0.001) and follow-up (86.5 ± 4.08 vs. 71.3 ± 4.83, p < 0.001). It also outperformed template training by 4-5% at post-training (81.6 ± 4.56 vs. 78.5 ± 4.65, p = 0.032) and follow-up (86.5 ± 4.08 vs. 82.7 ± 4.68, p = 0.004). For technical skills, the ChatGPT (4.0 ± 0.32) and template (4.0 ± 0.18) groups showed similar scores at post-training, outperforming traditional training (3.6 ± 0.50) by 11% (p < 0.001). At follow-up, ChatGPT (4.0 ± 0.18) and template (4.0 ± 0.32) still exceeded traditional training (3.8 ± 0.43) by 5% (p = 0.071, p = 0.026). Learning curve analysis revealed fastest knowledge (slope 13.02) and skill (slope 0.62) acquisition for ChatGPT group over template (slope 11.28, 0.38) and traditional (slope 5.17, 0.53). ChatGPT responses showed 100% relevance, 50% completeness, 60% accuracy, with 15.9 s response time. For training satisfaction, ChatGPT group had highest scores (4.2 ± 0.73), over template (3.8 ± 0.68) and traditional groups (2.6 ± 0.94) (p < 0.01).

Conclusion: Integrating AI, templates and digital imaging significantly improved puncture knowledge and skills over traditional training. Combining technological innovations and AI shows promise for streamlining complex medical competency mastery.

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利用生成式人工智能和数字技术加强穿刺技能培训:平行队列研究。
背景:针对进修医生的传统穿刺技能培训在效果和效率方面存在局限性。本研究探讨了如何应用生成式人工智能(ChatGPT)、模板和数字成像来加强穿刺技能培训。方法:90 名进修医生按顺序分为 3 组:传统培训组;模板和数字成像培训组;ChatGPT、模板和数字成像培训组。结果包括基线、培训后和 3 个月随访时测量的理论知识、技术技能和学员满意度:结果:在培训后,ChatGPT 组的理论知识得分比传统培训组提高了 17-21%(81.6 ± 4.56 vs. 69.6 ± 4.58,p 结论:ChatGPT 组的理论知识得分比传统培训组提高了 17-21%:与传统培训相比,人工智能、模板和数字成像的整合大大提高了穿刺知识和技能。将技术创新与人工智能相结合,有望简化复杂医疗能力的掌握。
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来源期刊
BMC Medical Education
BMC Medical Education EDUCATION, SCIENTIFIC DISCIPLINES-
CiteScore
4.90
自引率
11.10%
发文量
795
审稿时长
6 months
期刊介绍: BMC Medical Education is an open access journal publishing original peer-reviewed research articles in relation to the training of healthcare professionals, including undergraduate, postgraduate, and continuing education. The journal has a special focus on curriculum development, evaluations of performance, assessment of training needs and evidence-based medicine.
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