{
  "abstract": "Teaching film-reading is the most basic clinical teaching activity in the standardized residency training of laboratory medicine department. How to integrate theoretical knowledge with clinical cases and imaging data in a resident-centered manner remains a challenge. Problem-based learning (PBL) is a student-centered teaching method that fosters critical thinking and problem-solving skills. Considering that artificial intelligence technology has a wide range of application prospects in the field of medical education, we introduced a combined pedagogical package of PBL and ChatGPT in teaching film-reading. The aim of the study was to measure the effectiveness of the PBL-ChatGPT combined package in teaching film-reading for laboratory medicine residents in comparison with traditional teaching. Residents were randomly assigned to receive either the PBL-ChatGPT combined package (hereafter referred to as the PBL-ChatGPT group) or traditional teaching (traditional group). After each course, the members of each group were required to complete in-class quizzes and a standardized film-reading assessment form for residency training. Residents’ acceptance and satisfaction with the combined package were evaluated through a survey. Both the theoretical and film-reading examination scores were significantly higher in the PBL-ChatGPT group than in the traditional group (both P < 0.001), with large effect sizes (rank-biserial correlation 0.72 and 0.65, respectively). On the standardized film-reading assessment form, the PBL-ChatGPT group scored significantly higher in the preparation, methodology and overall evaluation domains (all P < 0.001, below the Bonferroni-corrected threshold of 0.0125 applied across the four pre-specified domains) and in the total score ( P < 0.001), whereas no significant difference was detected in the process domain ( P = 0.121). Residents’ feedback on the combined package was highly positive. The study indicates that the PBL-ChatGPT combined package holds significant potential as an innovative model for teaching film-reading to laboratory medicine residents.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 2,
  "authors": [
    {
      "name": "Sisi Feng"
    },
    {
      "name": "Baiyun Zhong"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/3dhn4xdnnmqm8.bib",
  "cited_by": 0,
  "doi": "10.1038/s41598-026-73717-y",
  "fulltext_url": null,
  "id": "3dhn4xdnnmqm8",
  "issns": [
    "2045-2322"
  ],
  "landing_url": "https://www.nature.com/articles/s41598-026-73717-y",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "2718|Artificial Intelligence in Healthcare and Education"
    ]
  ],
  "openalex": "W7215115813",
  "pdf_url": "https://www.nature.com/articles/s41598-026-73717-y_reference.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-01",
  "publisher": "Springer Science and Business Media LLC",
  "ris": "https://research.draconitic.com/paper/3dhn4xdnnmqm8.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:19Z",
      "native_id": "10.1038/s41598-026-73717-y",
      "source": "crossref",
      "url": "https://www.nature.com/articles/s41598-026-73717-y"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "openalex:2718|Artificial Intelligence in Healthcare and Education",
      "subfield": "epidemiology-public-health"
    }
  ],
  "title": "Application of a PBL-ChatGPT combined package in teaching film-reading for laboratory medicine residents",
  "title_text": "Application of a PBL-ChatGPT combined package in teaching film-reading for laboratory medicine residents",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:17:19Z",
  "url": "https://research.draconitic.com/paper/3dhn4xdnnmqm8",
  "venue": "Scientific Reports",
  "versions": []
}