{
  "abstract": "Objective: To compare patient perceptions of radiologist-written and artificial intelligence (AI)-generated simplifications of breast imaging reports in terms of simplicity, comprehensibility, empathy, and overall preference.\n\nMaterials and methods: In this prospective multicentre survey-based study, 10 anonymized breast imaging reports (5 ultrasound, 5 mammography; one BI-RADS category 1–5 per modality) were simplified by ChatGPT-4 within a 50-word limit. After assessment by a panel of 20 physicians, paired radiologist-written and AI-generated reports were evaluated by 314 patients (2965 evaluations) who rated simplicity, comprehensibility, and empathy on a 5-point Likert scale and indicated their preferred report. The origin of each report was concealed, and each pair was presented in randomized order. Univariable and multivariable mixed-effects logistic regression analyses were performed to identify factors associated with preferring AI-generated reports.\n\nResults: AI-generated reports were preferred in 63.3% (1876/2965) of evaluations. High simplicity and comprehensibility scores (4–5) were more frequently assigned to AI reports than to radiologist-written reports (70.0% vs 43.4% and 68.1% vs 40.4%, respectively). In multivariable analysis, higher comprehensibility was the strongest predictor in both directions: higher comprehensibility of AI-generated reports increased the odds of preferring AI (score 4: adjusted OR 1.87, 95% CI 1.08–3.23, p = 0.025), while higher comprehensibility of radiologist-written reports reduced them (score 4: adjusted OR 0.45, 95% CI 0.29–0.69, p < 0.001). Preference for AI-generated reports showed a non-significant trend toward increase with educational level.\n\nConclusion: AI-generated breast imaging reports were preferred by most patients and perceived as clearer, supporting a complementary and supervised use of AI-generated summaries.\n\nKey Points: Question Patients increasingly access their own radiology reports, yet standard radiologist-written reports are often difficult to understand, highlighting the need for patient-friendly alternatives.\n\nFindings In this study, AI-generated breast imaging reports were preferred by patients in 63.3% of evaluations, with comprehensibility emerging as the strongest predictor of preference.\n\nCritical relevance statement : AI-generated breast imaging reports were preferred by most patients and received higher ratings for simplicity and comprehensibility than radiologist-written reports, supporting a supervised and complementary use of AI-generated summaries in clinical breast imaging.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 17,
  "authors": [
    {
      "name": "Veronica Magni"
    },
    {
      "name": "Matilde Pavan"
    },
    {
      "name": "Maria Cosentino"
    },
    {
      "name": "Maurizio Cè"
    },
    {
      "name": "Andrea Cozzi"
    },
    {
      "name": "Maria Iodice"
    },
    {
      "name": "Alessandro Liguori"
    },
    {
      "name": "Marta Montesano"
    },
    {
      "name": "Sveva Mortellaro",
      "orcid": "0009-0004-3735-5494"
    },
    {
      "name": "Luca Nicosia"
    },
    {
      "name": "Mariassunta Roberta Pannarale"
    },
    {
      "name": "Filippo Pesapane"
    },
    {
      "name": "Giulia Quercioli"
    },
    {
      "name": "Giulia Querques"
    },
    {
      "name": "Gianpaolo Carrafiello"
    },
    {
      "name": "Sonia Santicchia"
    },
    {
      "name": "Serena Carriero"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/5jfay6pfwd4mw.bib",
  "cited_by": 0,
  "doi": "10.1186/s13244-026-02423-0",
  "fulltext_url": "https://europepmc.org/articles/PMC13728069",
  "id": "5jfay6pfwd4mw",
  "issns": [
    "1869-4101"
  ],
  "landing_url": "https://link.springer.com/article/10.1186/s13244-026-02423-0",
  "language": "eng",
  "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": [
    [
      "heading",
      "Original Article"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://link.springer.com/content/pdf/10.1186/s13244-026-02423-0.pdf",
  "pmcid": "PMC13728069",
  "pmid": "42970174",
  "published": "2026-11-02",
  "publisher": "Springer Science and Business Media LLC",
  "ris": "https://research.draconitic.com/paper/5jfay6pfwd4mw.ris",
  "sources": [
    {
      "first_seen": "2026-11-02T12:24:22Z",
      "native_id": "10.1186/s13244-026-02423-0",
      "source": "crossref",
      "url": "https://link.springer.com/article/10.1186/s13244-026-02423-0"
    },
    {
      "first_seen": "2026-11-04T15:20:43Z",
      "native_id": "MED:42970174",
      "source": "europepmc",
      "url": "https://europepmc.org/article/MED/42970174"
    },
    {
      "first_seen": "2026-11-06T12:23:49Z",
      "native_id": "PMC13728069",
      "source": "pmc",
      "url": "https://doi.org/10.1186/s13244-026-02423-0"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "issn:1869-4101",
      "subfield": "imaging"
    }
  ],
  "title": "Large language model–based simplification of breast imaging reports: a prospective multicentre study with 2965 patient-based evaluations",
  "title_text": "Large language model–based simplification of breast imaging reports: a prospective multicentre study with 2965 patient-based evaluations",
  "type": "peer-reviewed",
  "updated_at": "2026-11-07T12:21:07Z",
  "url": "https://research.draconitic.com/paper/5jfay6pfwd4mw",
  "venue": "Insights into Imaging",
  "versions": []
}