Of models and monocultures: epistemic risks of artificial intelligence in medical research
Abstract
Editorial articleReferences
1. Wang H, Fu T, Du Y, Gao W, Huang K, Liu Z, et al. Scientific discovery in the age of artificial intelligence. Nature. 2023;620(7972):47-60. https://doi.org/10.1038/s41586-023-06221-2
2. Kapoor MC. Navigating the Impact of Artificial Intelligence on Medical Writing. Ann Card Anaesth. 2025;28(2):105-6. https://doi.org/10.4103/aca.aca_14_25
3. Gallifant J, Afshar M, Ameen S, Aphinyanaphongs Y, Chen S, Cacciamani G, et al. The TRIPOD-LLM reporting guideline for studies using large language models. Nat Med. 2025;31(1):60-9. https://doi.org/10.1038/s41591-024-03425-5
4. Alvarado-Sánchez JI. The invisible weight of knowledge: epistemic inequity in global critical care research. Intensive Care Med. 2025;51;1724-6. https://doi.org/10.1007/s00134-025-08039-0
5. Messeri L, Crockett MJ. Artificial intelligence and illusions of understanding in scientific research. Nature. 2024;627(8002):49-58. https://doi.org/10.1038/s41586-024-07146-0
6. Pratt B, de Vries J. Where is knowledge from the global South? An account of epistemic justice for a global bioethics. J Med Ethics. 2023;49(5):325-34. https://doi.org/10.1136/jme-2022-108291
7. Bender EM, Gebru T, McMillan-Major A, Shmitchell S. On the Dangers of Stochastic Parrots. In: Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency. New York, NY, USA: ACM; 2021. p. 610-23. https://doi.org/10.1145/3442188.3445922
8. Alvero AJ, Lee J, Regla-Vargas A, Kizilcec RF, Joachims T, Antonio AL. Large language models, social demography, and hegemony: comparing authorship in human and synthetic text. J Big Data. 2024;11(1):138. https://doi.org/10.1186/s40537-024-00986-7
9. Santos B de S. Epistemologies of the South Justice Against Epistemicide. 2014.
10. Obermeyer Z, Powers B, Vogeli C, Mullainathan S. Dissecting racial bias in an algorithm used to manage the health of populations. Science (1979). 2019;366(6464):447-53. https://doi.org/10.1126/science.aax2342
11. Durán JM, Jongsma KR. Who is afraid of black box algorithms? On the epistemological and ethical basis of trust in medical AI. J Med Ethics. 2021;47:329-35. https://doi.org/10.1136/medethics-2020-106820
12. Obermeyer Z, Powers B, Vogeli C, Mullainathan S. Dissecting racial bias in an algorithm used to manage the health of populations. Science (1979). 2019;366(6464):447-53. https://doi.org/10.1126/science.aax2342
13. Vera-Baceta MA, Thelwall M, Kousha K. Web of Science and Scopus language coverage. Scientometrics. 2019;121(3):1803-13. https://doi.org/10.1007/s11192-019-03264-z
14. ELICIT. Information and advice from the Elicit team [Internet]. [cited 2025 Sep 22]. Available from: https://support.elicit.com/en/articles/553025
Downloads
Copyright (c) 2025 Sociedad Colombiana de Anestesiología y Reanimación (S.C.A.R.E.)

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
| Article metrics | |
|---|---|
| Abstract views | |
| Galley vies | |
| PDF Views | |
| HTML views | |
| Other views | |








