Generative artificial intelligence–mediated confirmation bias in health information seeking
Open accessAbstract
Generative artificial intelligence (GenAI) applications, such as ChatGPT, are transforming how individuals access health information, offering conversational and highly personalized interactions. While these technologies can enhance health literacy and decision-making, their capacity to generate deeply tailored-hypercustomized-responses risks amplifying confirmation bias by reinforcing pre-existing beliefs, obscuring medical consensus, and perpetuating misinformation, posing significant challenges to public health. This paper examines GenAI-mediated confirmation bias in health information seeking, driven by the interplay between GenAI's hypercustomization capabilities and users' confirmatory tendencies. Drawing on parallels with traditional online information-seeking behaviors, we identify three key "pressure points" where biases might emerge: query phrasing, preference for belief-consistent content, and resistance to belief-inconsistent information. Using illustrative examples, we highlight the limitations of existing safeguards and argue that even minor variations in applications' configuration (e.g., Custom GPT) can exacerbate these biases along those pressure points. Given the widespread adoption and fragmentation (e.g., OpenAI's GPT Store) of GenAI applications, their influence on health-seeking behaviors demands urgent attention. Since technical safeguards alone may be insufficient, we propose a set of interventions, including enhancing digital literacy, empowering users with critical engagement strategies, and implementing robust regulatory oversight. These recommendations aim to ensure the safe integration of GenAI into daily life, supporting informed decision-making and preserving the integrity of public understanding of health information.
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- Authors
- Lopez‐Lopez, E., Abels, C. M., Holford, D., Herzog, S. M., & Lewandowsky, S.
- Affiliations
- University of Potsdam (DE)
- University of Bristol (GB)
- Corresponding author
- Lopez‐Lopez, Ezequiel
- Venue
- Annals of the New York Academy of Sciences
- Publisher
- Wiley
- Published
- 2025-07-27
- Volume
- 1550
- Issue
- 1
- Pages
- 23-36
- ISSN
- 0077-8923 (ISSN-L)
- 1749-6632
- PMC
- PMC12412720
- OpenAlex
- W4412676335
- Cited by
- 38
- References
- 99
- Citations by year
- 2026: 35 · 2025: 3
- FWCI
- 2.25
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Annals of the New York Academy of Sciences (publishedVersion, cc-by)
- PubMed (publishedVersion)
- PubMed Central (publishedVersion, cc-by)
- Bristol Research (University of Bristol) (publishedVersion, cc-by)
- Funders
- Deutsche Forschungsgemeinschaft (458366841)
- Horizon 2020 Framework Programme (101094752, 964728)
- European Research Council (101020961)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Health Professions › General Health Professions › Health Literacy and Information Accessibility
- Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
- Keywords
- generative AI, confirmation bias, health information seeking, ChatGPT, personalized health information, health misinformation, belief consistency, digital literacy, AI regulation, health literacy, medical consensus
- MeSH terms
- Generative Artificial Intelligence, Artificial Intelligence, Decision Making, Humans, Bias, Health Literacy, Information Seeking Behavior
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.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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