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Phantom-based training of ultrasound-guided breast biopsy in medical education: a randomized controlled trial comparing handheld and high-end ultrasound

Background

Modern handheld ultrasound devices (HUDs) are becoming popular in medical education because they are small, portable, and relatively inexpensive. They provide clear images and are user-friendly, making them great tools for training beginners in ultrasound-guided procedures.

Study Objective

This study aimed to compare the effectiveness of HUDs with high-end ultrasound scanners (HEUS) in teaching medical students how to perform ultrasound-guided breast biopsies.

Methods

Thirty-two medical students without prior experience in performing these biopsies were divided into two groups: one using HUDs and the other using HEUS. After a training video, students practiced biopsies on a breast model. The main outcomes measured were how quickly they performed the procedure and how successful they were. Students also filled out a survey to share their thoughts on how easy the devices were to use and how much effort they felt it took.

Results

Students using HUDs had a slightly higher success rate (79.7%) compared to those using HEUS (68.8%). The time taken to perform the biopsies was similar for both devices. Students rated both devices equally in terms of ease of use and workload.

Conclusions

Using HUDs for teaching ultrasound-guided biopsies is effective. The performance, success rates, and user experience with HUDs are comparable to those with HEUS. Therefore, HUDs can be a cost-effective option for training in medical education.

Opportunities Based on Trial Data

  • Measurable Outcomes: Focus on success rates, performance times, and user satisfaction to assess training effectiveness.
  • Set Clear Goals: Aim for improved training outcomes in clinics and better patient care through enhanced medical education.
  • Select AI Tools: Choose AI solutions that support specific training and operational needs in ultrasound-guided procedures.
  • Implement Step by Step: Start with a pilot project using AI tools, monitor results, and expand based on real-world impacts.

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