Enhancing ultrasound training for breast cancer diagnosis: a controlled study of AI-assisted learning
- 1Department of Ultrasound, Affiliated Hospital of Southwest Jiaotong University, The Third People’s Hospital of Chengdu
- 2School of Computing and Artificial Intelligence, Southwest Jiaotong University
- 3Department of Breast and Thyroid Surgery, Affiliated Hospital of Southwest Jiaotong University, The Third People’s Hospital of Chengdu
Abstract
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.
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BibTeX
@article{wu2026enhancing,
title = {{Enhancing ultrasound training for breast cancer diagnosis: a controlled study of AI-assisted learning}},
author = {Shuang Wu and Weihao Wang and Jian Wu and Hong Zhou and Xun Gong and Ying Liu and Yang Zhou},
journal = {BMC Medical Education},
year = {2026},
month = jul,
doi = {10.1186/s12909-026-09958-2},
url = {https://doi.org/10.1186/s12909-026-09958-2},
note = {CC BY-NC-ND 4.0},
}
RIS
TY - JOUR
TI - Enhancing ultrasound training for breast cancer diagnosis: a controlled study of AI-assisted learning
AU - Shuang Wu
AU - Weihao Wang
AU - Jian Wu
AU - Hong Zhou
AU - Xun Gong
AU - Ying Liu
AU - Yang Zhou
PY - 2026
DA - 2026/07/01
JO - BMC Medical Education
DO - 10.1186/s12909-026-09958-2
UR - https://doi.org/10.1186/s12909-026-09958-2
AB - 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.
SN - 1472-6920
ER -