Reporting standards for the use of large language model-linked chatbots for health advice.
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Standardized frameworks are under development to establish detailed reporting standards to help authors design and report these studies.
“Standardized frameworks are under development to establish detailed reporting standards to help authors design and report these studies.18”
From Large Language Models for Chatbot Health Advice Studies: A Systematic Review (2025) · cites this paper as [18]
Cites
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- Authors
- Huo, B., Cacciamani, G. E., Collins, G. S., McKechnie, T., Lee, Y., & Guyatt, G.
- Affiliations
- McMaster University (CA)
- University of Southern California (US)
- Harvard University (US)
- Corresponding author
- Huo, Bright
- Venue
- Nature Medicine
- Published
- 2023-11-13
- Volume
- 29
- Issue
- 12
- Pages
- 2988-2988
- ISSN
- 1078-8956 (ISSN-L)
- 1546-170X
- OpenAlex
- W4388625750
- Cited by
- 53
- References
- 10
- Citations by year
- 2026: 5 · 2025: 25 · 2024: 20 · 2023: 2
- FWCI
- 2.08
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Nature Medicine (publishedVersion)
- PubMed (publishedVersion)
- University of Birmingham Research Portal (University of Birmingham) (submittedVersion)
- Funders
- Cancer Research UK (27294)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Clinical practice guidelines implementation
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
- Keywords
- large language models, chatbots, health advice, reporting guidelines, generative AI, health information accuracy, readability
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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