Generative-AI-Generated Challenges for Health Data Research
Open accessAbstract
Generative artificial intelligence (GenAI) promises to revolutionize data-driven fields (Milmo 2023). Building on decades of large language modeling (LLM) (Toner 2023), GenAI can collect, harmonize...
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The adoption of GenAI tools is now widespread, reshaping research practices in higher education, healthcare, and other high-stakes fields.
“The adoption of GenAI tools is now widespread, reshaping research practices in higher education, healthcare, and other high-stakes fields.”
From Utilizing Generative AI Responsibly and Ethically for Research Purposes in Higher Education: A Policy Analysis (2025) · cites this paper as Spector-Bagdady, 2023
Cites
On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? 🦜
Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021) · Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency · doi:10.1145/3442188.3445922 · Cited by 6,988 · cited 5× · see citing works
“…that by not documenting training data as LLMs are built will lead us to “incurring documentation debt i.e. putting our-selves in a situation where the datasets are both undocumented and too large to document post hoc” which is necessary for accountability and transparency (Bender et al. 2021).”
InfluentialArtificial Intelligence Risk Management Framework (AI RMF 1.0)
Tabassi, E. (2023) · doi:10.6028/nist.ai.100-1 · Cited by 1,136 · see citing works
Assessing risk, automating racism
Benjamin, R. (2019) · Science · doi:10.1126/science.aaz3873 · Cited by 378 · see citing works
Unreliable LLM bioethics assistants: Ethical and pedagogical risks
Goetz, L., Trengove, M., Trotsyuk, A., & Federico, C. A. (2023) · The American Journal of Bioethics · doi:10.1080/15265161.2023.2249843 · Cited by 12 · see citing works
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Alduais, A., Qadhi, S., Chaaban, Y., & Khraisheh, M. (2025) · Serials Review · doi:10.1080/00987913.2025.2581429 · Cited by 37 · see citing works
- Authors
- Spector-Bagdady, K.
- Affiliations
- University of Michigan Medical School
- Venue
- The American Journal of Bioethics
- Publisher
- Informa UK Limited
- Published
- 2023-10-03
- Volume
- 23
- Issue
- 10
- Pages
- 1-5
- ISSN
- 1526-5161 (ISSN-L)
- 1536-0075
- PMC
- PMC11024895
- OpenAlex
- W4387461516
- Cited by
- 18
- References
- 4
- Citations by year
- 2026: 2 · 2025: 9 · 2024: 6 · 2023: 1
- FWCI
- 0.72
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- The American Journal of Bioethics (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institutes of Health (UM1TR004404, R01TR004244)
- National Human Genome Research Institute (K01HG010496)
- National Center for Advancing Translational Sciences
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Physical Sciences › Computer Science › Artificial Intelligence › Law, AI, and Intellectual Property
- Keywords
- generative AI, health data research, large language models, data harmonization, data collection, bioethics, research ethics
- MeSH terms
- Bioethics, Humans
About these checks
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.
Statistics and arithmetic are recomputed, not judged.
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