Beyond the stereotypes: Artificial Intelligence image generation and diversity in anesthesiology

Mia Gisselbaek · Laurens Minsart · Ekin Köselerli · Mélanie Suppan · Başak Ceyda Meço · Laurence Seidel +4 more

21 citations0/0 files storedFrontiers in Artificial Intelligence

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Ethics and Social Impacts of AI Artificial Intelligence in Healthcare and Education Diversity and Career in Medicine

Abstract

Introduction: Artificial Intelligence (AI) is increasingly being integrated into anesthesiology to enhance patient safety, improve efficiency, and streamline various aspects of practice. Objective: This 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. Methods: This 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. Results: A 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. Conclusion: AI 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.

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2007The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) Statement: Guidelines for Reporting Observational Studies 2008The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies 2018Racial/ethnic discrimination and well-being during adolescence: A meta-analytic review.2000Understanding and addressing racial disparities in health care.2019Priorities and challenges for health leadership and workforce management globally: a rapid review 2023Bias in AI-based models for medical applications: challenges and mitigation strategies 2023An adversarial training framework for mitigating algorithmic biases in clinical machine learning 2023Algorithmic fairness and bias mitigation for clinical machine learning with deep reinforcement learning

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