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

Area of Science:

• Medical Education Technology
• Artificial Intelligence in Healthcare
• Surgical Skills Training

Background:

• Traditional puncture skills training methods present challenges in effectiveness and efficiency for refresher doctors.
• This study investigated the integration of generative AI (ChatGPT), digital templates, and imaging to overcome these limitations.

Purpose of the Study:

• To evaluate the impact of generative AI, templates, and digital imaging on improving theoretical knowledge, technical skills, and trainee satisfaction in puncture skills training.
• To compare the efficacy of AI-enhanced training with traditional methods and template-based digital training.

Main Methods:

• Ninety refresher doctors were divided into three groups: traditional training, template and digital imaging training, and ChatGPT, template, and digital imaging training.
• Outcomes including theoretical knowledge, technical skills, and trainee satisfaction were assessed at baseline, post-training, and at a 3-month follow-up.

Main Results:

• The ChatGPT group demonstrated significantly higher theoretical knowledge scores (17-21% increase) and technical skills compared to traditional training.
• AI-enhanced training also showed faster learning curve acquisition for both knowledge and skills.
• Trainee satisfaction was highest in the ChatGPT group, indicating a preference for the AI-integrated approach.

Conclusions:

• Integrating generative AI, templates, and digital imaging offers a substantial improvement over traditional methods for puncture skills acquisition.
• This combined technological approach shows significant potential for optimizing the mastery of complex medical skills and competencies.