The uncritical adoption of AI in science is alarming — we urgently need guard rails
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Researchers across disciplines express strong concerns regarding accuracy, bias, reproducibility, and the potential erosion of critical thinking if AI generated content is accepted without careful evaluation or potentially even used at all.
“Third, researchers across disciplines express strong concerns regarding accuracy, bias, reproducibility, and the potential erosion of critical thinking if AI generated content is accepted without careful evaluation or potentially even used at all 13 .”
From Reimagining biomedical science workflows in the age of large language models (2026) · cites this paper as [13]
Cites
Artificial intelligence and illusions of understanding in scientific research
Messeri, L., & Crockett, M. J. (2024) · Nature · doi:10.1038/s41586-024-07146-0 · Cited by 707 · see citing works
Self-citationArtificial intelligence tools expand scientists' impact but contract science's focus
Hao, Q., Xu, F., Li, Y., & Evans, J. (2026) · Nature · doi:10.1038/s41586-025-09922-y · Cited by 117 · see citing works
Towards end-to-end automation of AI research
Lu, C., Lu, C., Lange, R. T., Yamada, Y., Hu, S., Foerster, J., Ha, D., & Clune, J. (2026) · Nature · doi:10.1038/s41586-026-10265-5 · Cited by 116 · see citing works
Quantifying large language model usage in scientific papers
Liang, W., Zhang, Y., Wu, Z., Lepp, H., Ji, W., Zhao, X., Cao, H., Liu, S., He, S., Huang, Z., et al. (2025) · Nature Human Behaviour · doi:10.1038/s41562-025-02273-8 · Cited by 80 · see citing works
Mapping the Increasing Use of LLMs in Scientific Papers
Liang, W., Zhang, Y., Wu, Z., Lepp, H., Ji, W., Zhao, X., Cao, H., Liu, S., He, S., Huang, Z., et al. (2024) · arXiv · doi:10.48550/arxiv.2404.01268 · Cited by 55 · see citing works
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Reimagining biomedical science workflows in the age of large language models
Roberts, K. F., Koutarapu, S., Melendez, J., Landsness, E. C., Reese, J. T., Elbert, D. L., & Bateman, R. J. (2026) · npj Dementia · doi:10.1038/s44400-026-00126-3
“Third, researchers across disciplines express strong concerns regarding accuracy, bias, reproducibility, and the potential erosion of critical thinking if AI generated content is accepted without careful evaluation or potentially even used at all 13 .”
- Authors
- Messeri, L., & Crockett, M. J.
- Affiliations
- Yale University (US)
- Princeton University (US)
- Corresponding author
- Messeri, Lisa
- Crockett, M. J.
- Venue
- Nature
- Published
- 2026-05-19
- Volume
- 653
- Issue
- 8115
- Pages
- 675-676
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- OpenAlex
- W7161629784
- Cited by
- 7
- References
- 5
- Citations by year
- 2026: 7
- FWCI
- 40.40
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Nature (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
- Social Sciences › Decision Sciences › Information Systems and Management › Research, Science, and Academia
- Keywords
- artificial intelligence in science, artificial intelligence adoption, research integrity, scientific training, research output
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