{
  "abstract": "Background: AI chatbots now give people with diabetes diet advice, but whether those foods can be bought near home is rarely checked. Methods: In a cross-sectional ecological study of 11,974 California and Michigan census tracts, I linked diabetes prevalence (CDC PLACES 2025) with two USDA food-access maps: all SNAP-authorized stores (2025) and large grocery stores only (2019). I identified double-burden tracts (high diabetes and low income/low access) and hidden food deserts (low access to large grocery stores, but not to any SNAP store). In a pilot, ChatGPT, Gemini, and Claude generated meal plans for the same person across 20 high-diabetes tracts in Metro Los Angeles and Metro Detroit; I scored whether advice was healthy and whether foods were sold at nearby store types. Results: Double-burden tracts were few (71 in California, 19 in Michigan), almost none in the two metros. Food-desert tracts had a 1.5 percentage-point higher diabetes prevalence (95% CI, 1.0-2.0). Counting all SNAP stores hid 567 Californian and 329 Michigan food deserts; in Metro Detroit, all 97 disappeared, including 51 high-diabetes tracts with about 19,500 adults with diabetes. Chatbot advice was healthy, but in hidden tracts 30% of recommended foods were not reliably sold nearby; the chatbots barely adjusted. Of 55 places they named, 13% had errors, and most were over a mile away. Conclusions: The current USDA map obscures food deserts, especially in Detroit, and AI diet advice barely adjusts for them. AI nutrition tools for diabetes should include a place-aware check built on public food-access data.",
  "abstract_shown": true,
  "affiliations": [
    "University of the Cumberlands"
  ],
  "article_type": "PUBLISHAHEADOFPRINT",
  "arxiv": null,
  "author_total": 1,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "S. S. Chitipolu"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/a4fjjbczznbyx.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.11.03.26367208",
  "fulltext_url": "https://www.medrxiv.org/content/10.64898/2026.11.03.26367208v1.full",
  "id": "a4fjjbczznbyx",
  "issns": [],
  "landing_url": "https://www.medrxiv.org/content/10.64898/2026.11.03.26367208v1",
  "language": null,
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "medrxiv",
      "epidemiology"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://www.medrxiv.org/content/10.64898/2026.11.03.26367208v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-11-03",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/a4fjjbczznbyx.ris",
  "sources": [
    {
      "first_seen": "2026-11-05T00:20:42Z",
      "native_id": "10.64898/2026.11.03.26367208",
      "source": "medrxiv",
      "url": "https://www.medrxiv.org/content/10.64898/2026.11.03.26367208v1"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "medrxiv:epidemiology",
      "subfield": "epidemiology-public-health"
    }
  ],
  "title": "Before AI tells patients what to eat, it needs to know where they live: a cross-sectional ecological study of diabetes and food access in California and Michigan census tracts, with a spotlight on Metro Detroit and Metro Los Angeles",
  "title_text": "Before AI tells patients what to eat, it needs to know where they live: a cross-sectional ecological study of diabetes and food access in California and Michigan census tracts, with a spotlight on Metro Detroit and Metro Los Angeles",
  "type": "preprint",
  "updated_at": "2026-11-06T00:20:58Z",
  "url": "https://research.draconitic.com/paper/a4fjjbczznbyx",
  "venue": "medRxiv",
  "versions": []
}