Study Notes on AI and Themes of Nursing Representation

Artificial Intelligence and Images Portraying Nurses Through the Decades

Authors

• Janet Reed, PhD, RN, CMSRN

• Tracy M. Dodson, PhD, RN

• Amy B. Petrinec, PhD, RN

• Delaney Tennant, BSN graduate

• Jenna Chmelik, BSN, RN

• Shawnna Cripple, BSN graduate

• Published on May 31, 2025

• DOI: 10.3912/OJIN.Vol30No02Man05

Abstract

• The public image of nursing is affected by outdated stereotypes.

• Rapid advancement of AI technologies has potential to reshape perceptions.

• Study examines 288 GAI images of nurses using quantitative content analysis.

Key Findings

• Significant Differences Noted: Between three image generators regarding:

  • Gender representation

  • Ethnic diversity

  • Sexualization of images

• Highlighted need for critical evaluation of AI imagery to combat biases and stereotypes.

Introduction

• Despite being the most trusted healthcare profession, nursing faces outdated perceptions.

• Nurses have transitioned from a marginalized role to skilled professionals with autonomous responsibilities.

• Persistent stereotypes in media continue to undermine the profession, e.g., “angel in white,” “naughty nurse.”

• Negative portrayals are linked to recruitment challenges and patient harm.

Historical Context

• Origins of Modern Nursing:

  • Rooted in religious service and military care.

  • 19th-century shift away from undesirability to recognition via Florence Nightingale's contributions.

• Media portrayals throughout history have perpetuated harmful stereotypes.

Portrayals of Nurses

• Common stereotypes include:

  • Nurse as the doctor's handmaiden

  • Ministering angel

  • Battle-ax character

• Appearances in media affecting public perception:

  • Films and shows such as MAS*H and One Flew Over the Cuckoo’s Nest.

  • Negative effects on recruitment and job satisfaction.

The Naughty Nurse Stereotype

• The sexualization of nurses is widespread.

• Represents nurses as sexual objects rather than competent professionals.

• Roots in 1950s/60s media and persists in modern culture.

Research Methodology

Aim

• To systematically analyze AI-generated images of nurses across history.

Literature Review

• Databases: PubMed, CINAHL, Medline, Google Scholar.

• Search terms focused on public image and stereotypes of nursing.

Methods

• Study Design: Non-human research approval; utilized quantitative content analysis.

• Generated a dataset of 288 images using three platforms (Midjourney, Adobe Firefly, ImageFx).

• Analyzed images across decades with standardized prompts.

Analysis

Coding Scheme

• Developed a coding scheme for systematic analysis.

• Variables Included:

  • Gender

  • Ethnicity

  • Professionalism

  • Sexualization

  • Context and realism of images

• Interrater reliability tested with 90.8% agreement on coding.

Results Overview

RQ1: Visual Elements in Generative AI Images

• Ethnicity Representation:

  • 83% light skin tone

  • 9.4% dark skin tone

  • 3.1% Asian

  • 4.5% mixed/medium tone

• Gender Representation:

  • 86.5% female nurses

  • 13.5% male nurses

• Facial Expressions:

  • 37.8% happy

  • 59.3% serious

• Sexualization:

  • 28.8% depicted the “Naughty Nurse” stereotype;

  • 7% classified as unprofessional due to oversexualization.

• Common Objects:

  • 44.8% included stethoscopes (28.5% were unrealistic).

RQ2: Gender and Race Representation Across Image Generators

• Statistically significant differences found among platforms:

  • Gender Diversity: Adobe (61.5%), Midjourney (100%), ImageFx (97.9%).

  • Ethnic Diversity: Adobe (28.2% non-White), Midjourney (1.1%), ImageFx (11.7%).

  • Sexualization Rate: Midjourney (52.1%), Adobe (16.7%), ImageFx (17.7%).

Discussion

• Implications of Findings:

  • Reinforcement of outdated gender roles can deter male entrants into nursing.

  • Unrealistic beauty standards presented in AI images perpetuate detrimental stereotypes.

• Future Recommendations:

  • Focus on promoting accuracy and diversity in nursing imagery to enhance recruitment.

  • Utilize specific prompts in GAI to create more inclusive representations.

Conclusion

• AI-generated images perpetuate implicit biases and stereotypes in nursing.

• Highlighting racial and gender diversity is crucial for improving the public image of nursing.

• Moving towards an accurate representation is vital for recruitment and representation within the profession.

Authors' Backgrounds

• Janet Reed specializes in medical-surgical nursing with research on technology in nursing education.

• Tracy M. Dodson focuses on education technologies and nursing curricula.

• Amy B. Petrinec is engaged in research related to caregiver outcomes in nursing.

• Delaney Tennant participated as a research assistant; Jenna Chmelik studies to become a nurse educator; Shawnna Cripple contributed in similar capacities.

References

• Continue to use cited works (2023, 2024) related to the topics and findings of this study, exploring the impact of stereotypes on perception and the roles of nurses in society.