[en] IntroductionArtificial Intelligence (AI) is increasingly being integrated into anesthesiology to enhance patient safety, improve efficiency, and streamline various aspects of practice.ObjectiveThis study aims to evaluate whether AI-generated images accurately depict the demographic racial and ethnic diversity observed in the Anesthesia workforce and to identify inherent social biases in these images.MethodsThis cross-sectional analysis was conducted from January to February 2024. Demographic data were collected from the American Society of Anesthesiologists (ASA) and the European Society of Anesthesiology and Intensive Care (ESAIC). Two AI text-to-image models, ChatGPT DALL-E 2 and Midjourney, generated images of anesthesiologists across various subspecialties. Three independent reviewers assessed and categorized each image based on sex, race/ethnicity, age, and emotional traits.ResultsA total of 1,200 images were analyzed. We found significant discrepancies between AI-generated images and actual demographic data. The models predominantly portrayed anesthesiologists as White, with ChatGPT DALL-E2 at 64.2% and Midjourney at 83.0%. Moreover, male gender was highly associated with White ethnicity by ChatGPT DALL-E2 (79.1%) and with non-White ethnicity by Midjourney (87%). Age distribution also varied significantly, with younger anesthesiologists underrepresented. The analysis also revealed predominant traits such as “masculine, ““attractive, “and “trustworthy” across various subspecialties.ConclusionAI models exhibited notable biases in gender, race/ethnicity, and age representation, failing to reflect the actual diversity within the anesthesiologist workforce. These biases highlight the need for more diverse training datasets and strategies to mitigate bias in AI-generated images to ensure accurate and inclusive representations in the medical field.
Disciplines :
Anesthesia & intensive care
Author, co-author :
Gisselbaek, Mia; Geneva University Hospitals > Department of Anesthesiology, Clinical Pharmacology, Intensive Care and Emergency Medicine
Minsart, Laurens; UZA - University of Antwerp Hospital [BE] > Department of Anesthesia
Köselerli, Ekin; University of Ankara School of Medicine > Department of Anesthesiology and Intensive Care Unit
Suppan, Mélanie; Geneva University Hospitals > Department of Anesthesiology, Clinical Pharmacology, Intensive Care and Emergency Medicine
Meco, Basak Ceyda; University of Ankara School of Medicine > Department of Anesthesiology and Intensive Care Unit
Seidel, Laurence ; Université de Liège - ULiège > Département des sciences de la santé publique
Albert, Adelin ; Université de Liège - ULiège > Département des sciences de la santé publique
Barreto Chang, Odmara L.; UCSF - University of California, San Francisco [US-CA] > Department of Anesthesia and Perioperative Care
Saxena, Sarah; AZ Sint-Jan Brugge Oostende > Department of Anesthesia and Reanimation
Berger-Estilita, Joana; UniBE - University of Berne [CH] > Institute for Medical Education
Language :
English
Title :
Beyond the stereotypes: Artificial Intelligence image generation and diversity in anesthesiology
Ali R. Tang O. Y. Connolly I. D. Abdulrazeq H. F. Mirza F. N. Lim R. K. et al. (2024). Demographic representation in 3 leading artificial intelligence text-to-image generators. JAMA Surg. 159, 87–95. doi: 10.1001/jamasurg.2023.5695, PMID: 37966807
American Society of Anesthesiologists. (2024). MSC statement of commitment to DEI. Available at: https://www.asahq.org/education-and-career/asa-medical-student-component/diversity-equity-and-inclusion (Accessed June 25, 2024)
Australia and New Zealand College of Anaesthesia (2023). ANZCA diversity, equity and inclusion (DEI) background paper. Available at: https://www.anzca.edu.au/getattachment/28d741b6-2c01-420e-96b6-922bf22ffb8c/ANZCA-Diversity,-Equity,-and-Inclusion-(DEI)-background-paper (Accessed June 25, 2024).
Benner A. D. Wang Y. Shen Y. Boyle A. E. Polk R. Cheng Y. P. (2018). Racial/ethnic discrimination and well-being during adolescence: a meta-analytic review. Am. Psychol. 73, 855–883. doi: 10.1037/amp0000204, PMID: 30024216
Berger-Estilita J. Leitl J. Vacas S. Neskovic V. Stuber F. Zdravkovic M. (2023). Welfare practices for anaesthesiology trainees in Europe: a descriptive cross-sectional survey study. Eur. J. Anaesthesiol. 40, 105–112. doi: 10.1097/EJA.0000000000001787, PMID: 36503934
Diehl A. Dzubinski L. Stephenson A. (2023). Women in leadership face ageism at every age. hbr.org. Available at: https://hbr.org/2023/06/women-in-leadership-face-ageism-at-every-age (Accessed April 8, 2024).
Figueroa C. A. Harrison R. Chauhan A. Meyer L. (2019). Priorities and challenges for health leadership and workforce management globally: a rapid review. BMC Health Serv. Res. 19:239. doi: 10.1186/s12913-019-4080-7, PMID: 31014349
Geneviève L. D. Martani A. Shaw D. Elger B. S. Wangmo T. (2020). Structural racism in precision medicine: leaving no one behind. BMC Med. Ethics 21:17. doi: 10.1186/s12910-020-0457-8, PMID: 32075640
Gisselbaek M. Köselerli E. Suppan M. Minsart L. Meco B. Seidel L. et al. (2024). Gender bias in images of anaesthesiologists generated by artificial intelligence. Br. J. Anaesthesia 133, 692–695. doi: 10.1016/j.bja.2024.05.027, PMID: 38942642
Goetschalckx L. Andonian A. Wagemans J. (2021). Generative adversarial networks unlock new methods for cognitive science. Trends Cogn. Sci. 25, 788–801. doi: 10.1016/j.tics.2021.06.006, PMID: 34364792
Hayasaka T. Kawano K. Kurihara K. Suzuki H. Nakane M. Kawamae K. (2021). Creation of an artificial intelligence model for intubation difficulty classification by deep learning (convolutional neural network) using face images: an observational study. J. Intensive Care 9:38. doi: 10.1186/s40560-021-00551-x, PMID: 33952341
Laake J. H. Astvad M. Bentsen G. Escher C. Haney M. Hoffmann-Petersen J. et al. (2022). A policy for diversity, equity, inclusion and anti-racism in the Scandinavian Society of Anaesthesiology and Intensive Care Medicine (SSAI). Acta Anaesthesiol. Scand. 66, 141–144. doi: 10.1111/aas.13978, PMID: 34462910
Louwen C. Reidlinger D. Milne N. (2023). Profiling health professionals' personality traits, behaviour styles and emotional intelligence: a systematic review. BMC Med. Educ. 23:120. doi: 10.1186/s12909-023-04003-y, PMID: 36803372
Ma D. S. Correll J. Wittenbrink B. (2015). The Chicago face database: a free stimulus set of faces and norming data. Behav. Res. Methods 47, 1122–1135. doi: 10.3758/s13428-014-0532-5, PMID: 25582810
Marinucci L. Mazzuca C. Gangemi A. (2023). Exposing implicit biases and stereotypes in human and artificial intelligence: state of the art and challenges with a focus on gender. AI & Soc. 38, 747–761. doi: 10.1007/s00146-022-01474-3
Mittermaier M. Raza M. M. Kvedar J. C. (2023). Bias in AI-based models for medical applications: challenges and mitigation strategies. NPJ Digit. Med. 6:113. doi: 10.1038/s41746-023-00858-z
Nicoletti L. Bass D. (2024). Humans are biased. Generative AI is even more. Bloomberg. Available at: https://www.bloomberg.com/graphics/2023-generative-ai-bias/ (Accessed April 4, 2024).
Pittman P. Chen C. Erikson C. Salsberg E. Luo Q. Vichare A. et al. (2021). Health workforce for health equity. Med. Care 59, S405–s408. doi: 10.1097/mlr.0000000000001609, PMID: 34524235
Raymer M. Reed M. Spiegel M. Purvanova R. K. (2017). An examination of generational stereotypes as a path towards reverse ageism. Psychol. Manager J. 20, 148–175. doi: 10.1037/mgr0000057
Reeve P. E. (1980). Personality characteristics of a sample of anaesthetists. Anaesthesia 35, 559–568. doi: 10.1111/j.1365-2044.1980.tb03852.x
Schäfer M. S. (2023). The notorious GPT: science communication in the age of artificial intelligence. JCOM. J. Sci. Commun. 22, 1–15. doi: 10.22323/2.22020402
Shen S. Chen Y. Hua M. Ye M. (2023). Measuring designers ‘use of Midjourney on the technology acceptance model’. Conference Proceeding, Milan, Italy: IASDR 2023, Life-Changing Design, 9-13 October.
Stosic M. D. Blanch-Hartigan D. Ruben M. A. Meyer E. C. Henig A. Waisel D. B. et al. (2023). “You look young! Are you a doctor?” A qualitative content analysis of anesthesiology resident responses to ageism in clinical encounters. Anesthesiology 139, 667–674. doi: 10.1097/aln.0000000000004730
Strzelecki A. Cicha K. Rizun M. Rutecka P. (2024). Acceptance and use of ChatGPT in the academic community. Educ. Inf. Technol. doi: 10.1007/s10639-024-12765-1
Stypińska J. Franke A. (2022). AI revolution in healthcare and medicine and the (re-)emergence of inequalities and disadvantages for ageing population. Front. Sociol. 7:1038854. doi: 10.3389/fsoc.2022.1038854, PMID: 36755564
Tang O. Y. Ali R. Connolly I. D. Fridley J. S. Zadnik Sullivan P. L. Cielo D. et al. (2023). Letter: the urgency of neurosurgical leadership in the era of artificial intelligence. Neurosurgery 93, e69–e70. doi: 10.1227/neu.0000000000002576, PMID: 37319400
Tomlinson B. Black R. W. Patterson D. J. Torrance A. W. (2024). The carbon emissions of writing and illustrating are lower for AI than for humans. Sci. Rep. 14:3732. doi: 10.1038/s41598-024-54271-x, PMID: 38355820
Van Der Wal R. A. B. Wallage J. Scheffer G. Prins J. B. Bijleveld E. (2022). Personality in anaesthesiologists, a systematic review of the literature. Eur. J. Anaesthesiol. 39, 378–387. doi: 10.1097/eja.0000000000001650, PMID: 35232934
Von Elm E. Altman D. G. Egger M. Pocock S. J. Gøtzsche P. C. Vandenbroucke J. P. (2008). The strengthening the reporting of observational studies in epidemiology (STROBE) statement: guidelines for reporting observational studies. J. Clin. Epidemiol. 61, 344–349. doi: 10.1016/j.jclinepi.2007.11.008
Wang J. Shaheen M. (2022). Impact of minority perceived discrimination on resistance to innovation and moderating role of psychological distress: evidence from ethnic minority students of China. Front. Psychol. 13:989961. doi: 10.3389/fpsyg.2022.989961, PMID: 36267059
Williams D. R. Rucker T. D. (2000). Understanding and addressing racial disparities in health care. Health Care Financ. Rev. 21, 75–90, PMID: 11481746
World Medical Association. (2013). WMA declaration of Helsinki: ethical principles for medical research involving human subjects.
Yang J. Soltan A. A. S. Eyre D. W. Clifton D. A. (2023a). Algorithmic fairness and bias mitigation for clinical machine learning with deep reinforcement learning. Nat. Mach. Intell. 5, 884–894. doi: 10.1038/s42256-023-00697-3, PMID: 37615031
Yang J. Soltan A. A. S. Eyre D. W. Yang Y. Clifton D. A. (2023b). An adversarial training framework for mitigating algorithmic biases in clinical machine learning. npj Dig. Med. 6:55. doi: 10.1038/s41746-023-00805-y, PMID: 36991077
Zdravkovic M. Osinova D. Brull S. J. Prielipp R. C. Simões C. M. Berger-Estilita J. (2020). Perceptions of gender equity in departmental leadership, research opportunities, and clinical work attitudes: an international survey of 11 781 anaesthesiologists. Br. J. Anaesth. 124, e160–e170. doi: 10.1016/j.bja.2019.12.022, PMID: 32005515
