Personality in anaesthesiologists, a systematic review of the literature

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

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.

Analysis on Burnout, Job Conditions, Alexithymia, and Other Psychological Symptoms in a Sample of Italian Anesthesiologists and Intensivists, Assessed Just before the COVID-19 Pandemic: An AAROI-EMAC Study

Background. It was previously reported that health care professionals working in the fields of anesthesiology and emergency are at higher risk of burnout. However, the correlations between burnout, alexithymia, and other psychological symptoms are poorly investigated. Furthermore, there is a lack of evidence on which risk factors, specific to the work of anesthetists and intensivists, can increase the risk of burnout, and which are useful for developing remedial health policies. Methods. This cross-sectional study was conducted in 2020 on a sample of 300 professionals recruited from AAROI-EMAC subscribers in Italy. Data collection instruments were a questionnaire on demographic, education, job characteristics and well-being, the Maslach Burnout Inventory Tool, the Toronto Alexithymia Scale, the Symptom Checklist-90-R, and the Rosenberg Self-Esteem Scale administered during refresher courses in anesthesiology. Correlations between burnout and physical and psychological symptoms were searched. Results. With respect to burnout, 29% of individuals scored at high risk on emotional exhaustion, followed by 36% at moderate-high risk. Depersonalization high and moderate-high risk were scored by 18.7% and 34.3% of individuals, respectively. Burnout personal accomplishment was scored by 34.7% of respondents. The highest mean scores of burnout dimensions were related to dissatisfaction with one's career, conflicting relationships with surgeons, and, finally, difficulty in explaining one's work to patients. Conclusions. Burnout rates in Italian anesthesiologists and intensivists have been worrying since before the COVID-19 pandemic. Anesthesiologists with higher levels of alexithymia are more at risk for burnout. It is therefore necessary to take urgent health policy measures..