{
  "abstract": "BackgroundEstimates of generative artificial intelligence (AI) use in the medical literature vary widely, potentially reflecting differences across journals. We estimated changes in the prevalence of AI-generated writing over time using abstracts from 12 general medical journals.\n\nMethodsWe randomly sampled up to 200 abstracts per journal-year from publications dated January 1, 2020, through August 8, 2026. Abstracts were scored using GPTZero, with journal-specific thresholds calibrated to fix a 1% false-positive rate (FPR) in the pre-AI baseline period (2020-2021). We estimated annual detector-positive proportions and calculated a publication-volume-weighted estimate across sampled journals. Associations with journal impact factor, publication model, and publication volume were examined descriptively.\n\nResultsAmong 15,268 abstracts, estimated prevalence of AI-generated text increased after 2022, with substantial variation in timing and magnitude across journals. In 2026 through August 8, prevalence estimates ranged from 5.7% (95% CI, 2.1%-10.0%) in the New England Journal of Medicine to 83.5% (95% CI, 77.1%-87.5%) in Frontiers in Medicine. The publication-volume-weighted estimate across sampled journals was 66.7% (95% CI, 64.0%-69.2%), and trends were insensitive to specification of false-positive rate in baseline data. The proportion of abstracts showing signs of AI-generated text was lower in journals with higher impact factors and higher in fully open-access journals.\n\nConclusionsEstimated prevalence of AI-generated text increased substantially across all sampled medical journals, with marked between-journal differences associated with journal impact factor and publication model. These findings support development of standardized, transparent AI reporting in medical publishing.",
  "abstract_shown": true,
  "affiliations": [
    "Brown University"
  ],
  "article_type": "PUBLISHAHEADOFPRINT",
  "arxiv": null,
  "author_total": 4,
  "authors": [
    {
      "name": "M. D. Flanders"
    },
    {
      "name": "M. Caunca"
    },
    {
      "name": "A. Mehrotra"
    },
    {
      "affs": [
        0
      ],
      "name": "S. F. Ackley"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/erwf343ggeedd.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.10.24.26366055",
  "fulltext_url": "https://www.medrxiv.org/content/10.64898/2026.10.24.26366055v1.full",
  "id": "erwf343ggeedd",
  "issns": [],
  "landing_url": "https://www.medrxiv.org/content/10.64898/2026.10.24.26366055v1",
  "language": null,
  "licence": "CC-BY-NC",
  "licence_label": "CC BY-NC",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "medrxiv",
      "health informatics"
    ],
    [
      "openalex",
      "3311|Academic integrity and plagiarism"
    ]
  ],
  "openalex": "W7223695887",
  "pdf_url": "https://www.medrxiv.org/content/10.64898/2026.10.24.26366055v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-24",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/erwf343ggeedd.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:21:46Z",
      "native_id": "10.64898/2026.10.24.26366055",
      "source": "medrxiv",
      "url": "https://www.medrxiv.org/content/10.64898/2026.10.24.26366055v1"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "medrxiv:health informatics",
      "subfield": "epidemiology-public-health"
    }
  ],
  "title": "AI-Assisted Writing Across Medical Journals: Heterogeneity in Prevalence and Trends",
  "title_text": "AI-Assisted Writing Across Medical Journals: Heterogeneity in Prevalence and Trends",
  "type": "preprint",
  "updated_at": "2026-10-30T12:21:46Z",
  "url": "https://research.draconitic.com/paper/erwf343ggeedd",
  "venue": "medRxiv",
  "versions": []
}