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Objectives <p>This study aimed to develop and evaluate an AI-assisted teaching platform to enhance diagnostic competency in breast ultrasound. The goal was to assess whether AI integration improves diagnostic accuracy, learning efficiency, and participant satisfaction within a residency training program.</p> Methods <p>We conducted a cohort-based study at our hospital. Twelve junior residents (experimental group) underwent AI-assisted training via a newly implemented platform, while twelve senior residents (control group) completed conventional training. Diagnostic performance was evaluated before and after the one-month intervention using consistent assessments. Participant satisfaction was surveyed across domains including learning engagement, skill development, and confidence.</p> Results <p>In the experimental group, post-intervention diagnostic scores (90.50 ± 9.82) were significantly higher than pre-intervention diagnostic scores(70.00 ± 17.55, <i>P</i> = 0.003,95%CI[-32.54,-8.46], Cohen’s d=-1.44). Survey results indicated high satisfaction: 83.33% strongly agreed the platform facilitated learning, 66.67% reported improved pattern recognition, and 66.67% noted increased engagement in self-learning. A majority also reported gains in clinical reasoning and confidence when facing a real patient.</p> Conclusions <p>We integrated an AI-assisted platform into ultrasound residency training, creating an educational tool. In this single-center exploratory study, the AI-assisted platform shows potential to improve residents’ diagnostic skills for breast ultrasound.</p>

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Enhancing ultrasound training for breast cancer diagnosis: a controlled study of AI-assisted learning

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月17日

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  • Shuang Wu,
  • Weihao Wang,
  • Jian Wu,
  • Hong Zhou,
  • Xun Gong,
  • Ying Liu,
  • Yang Zhou

摘要

Objectives

This study aimed to develop and evaluate an AI-assisted teaching platform to enhance diagnostic competency in breast ultrasound. The goal was to assess whether AI integration improves diagnostic accuracy, learning efficiency, and participant satisfaction within a residency training program.

Methods

We conducted a cohort-based study at our hospital. Twelve junior residents (experimental group) underwent AI-assisted training via a newly implemented platform, while twelve senior residents (control group) completed conventional training. Diagnostic performance was evaluated before and after the one-month intervention using consistent assessments. Participant satisfaction was surveyed across domains including learning engagement, skill development, and confidence.

Results

In the experimental group, post-intervention diagnostic scores (90.50 ± 9.82) were significantly higher than pre-intervention diagnostic scores(70.00 ± 17.55, P = 0.003,95%CI[-32.54,-8.46], Cohen’s d=-1.44). Survey results indicated high satisfaction: 83.33% strongly agreed the platform facilitated learning, 66.67% reported improved pattern recognition, and 66.67% noted increased engagement in self-learning. A majority also reported gains in clinical reasoning and confidence when facing a real patient.

Conclusions

We integrated an AI-assisted platform into ultrasound residency training, creating an educational tool. In this single-center exploratory study, the AI-assisted platform shows potential to improve residents’ diagnostic skills for breast ultrasound.