{
  "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.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Ultrasound, Affiliated Hospital of Southwest Jiaotong University, The Third People’s Hospital of Chengdu",
    "School of Computing and Artificial Intelligence, Southwest Jiaotong University",
    "Department of Breast and Thyroid Surgery, Affiliated Hospital of Southwest Jiaotong University, The Third People’s Hospital of Chengdu"
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 7,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Shuang Wu"
    },
    {
      "affs": [
        1
      ],
      "name": "Weihao Wang"
    },
    {
      "affs": [
        2
      ],
      "name": "Jian Wu"
    },
    {
      "affs": [
        0
      ],
      "name": "Hong Zhou"
    },
    {
      "affs": [
        1
      ],
      "name": "Xun Gong"
    },
    {
      "affs": [
        0
      ],
      "name": "Ying Liu"
    },
    {
      "affs": [
        0
      ],
      "name": "Yang Zhou"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/ez5jb0degm0cf.bib",
  "cited_by": 0,
  "doi": "10.1186/s12909-026-09958-2",
  "fulltext_url": "https://doi.org/10.1186/s12909-026-09958-2",
  "id": "ez5jb0degm0cf",
  "issns": [
    "1472-6920"
  ],
  "landing_url": "https://doi.org/10.1186/s12909-026-09958-2",
  "language": "en",
  "licence": "CC-BY-NC-ND-4.0",
  "licence_label": "CC BY-NC-ND 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "heading",
      "Research"
    ],
    [
      "lcc",
      "LC8-6691"
    ],
    [
      "lcc",
      "R"
    ],
    [
      "openalex",
      "2718|Artificial Intelligence in Healthcare and Education"
    ]
  ],
  "openalex": "W7168964242",
  "pdf_url": null,
  "pmcid": "PMC13721689",
  "pmid": "42464241",
  "published": "2026-07-01",
  "publisher": "BMC",
  "ris": "https://research.draconitic.com/paper/ez5jb0degm0cf.ris",
  "sources": [
    {
      "first_seen": "2026-11-01T14:21:22Z",
      "native_id": "022b20959e69427fb565b77be997686c",
      "source": "doaj",
      "url": "https://doi.org/10.1186/s12909-026-09958-2"
    },
    {
      "first_seen": "2026-11-03T12:22:20Z",
      "native_id": "PMC13721689",
      "source": "pmc",
      "url": "https://doi.org/10.1186/s12909-026-09958-2"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "openalex:2718|Artificial Intelligence in Healthcare and Education",
      "subfield": "epidemiology-public-health"
    }
  ],
  "title": "Enhancing ultrasound training for breast cancer diagnosis: a controlled study of AI-assisted learning",
  "title_text": "Enhancing ultrasound training for breast cancer diagnosis: a controlled study of AI-assisted learning",
  "type": "peer-reviewed",
  "updated_at": "2026-11-03T14:20:49Z",
  "url": "https://research.draconitic.com/paper/ez5jb0degm0cf",
  "venue": "BMC Medical Education",
  "versions": []
}