Authors
Shuang Wu, Weihao Wang, Jian Wu, Hong Zhou, Xun Gong, Ying Liu, Yang Zhou
Published in
BMC medical education. Jul 17, 2026. Epub Jul 17, 2026.
Abstract
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.
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.
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.
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.
PMID:
42464241
Bibliographic data and abstract were imported from PubMed on 17 Jul 2026.
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