Gender Differences in the Use of ChatGPT as Generative Artificial Intelligence for Clinical Research and Decision-Making in Occupational Medicine

Healthcare, vol. 13, pp. 1394

Abstract

Background/Objectives: Artificial intelligence (AI) has evolved from early diagnostic expert systems to advanced generative models, such as GPT-4, which are increasingly being used in healthcare. Concerns persist regarding inaccuracies and input dependency. This study aimed to deliver initial insights into whether gender influences the interaction of medical professionals with generative AI. Methods: This analysis investigated gender differences in medical students’ and physicians’ interactions with ChatGPT-4 while researching occupational medicine cases in a randomized controlled study. Participants assessed cases involving asbestos-related disease, metal sulfate allergy, and berylliosis using ChatGPT. Inputs and outputs were evaluated for accuracy, confabulations, communication styles, and user satisfaction. Demographic data and self-assessments of occupational medicine knowledge before and after the tasks were also collected. Results: Among 27 participants (63% women, 37% men), women showed greater knowledge improvement after using ChatGPT, particularly in asbestos-related cancer identification. No significant gender differences emerged in diagnostic accuracy, reporting procedures, or satisfaction with ChatGPT. Women exhibited significantly higher self-rated competence after using the ChatGPT application, while men only showed minimal change. Input from the female participants led to more confabulations, although response accuracy remained comparable. Conclusions: This study offers the first real-world insights into the use of generative AI in occupational medicine, highlighting the importance of understanding user-dependent variability in AI-supported clinical practice and decision-making. These findings underscore the need for gender-sensitive AI literacy training in medical education, accommodating diverse interaction styles and strategies to mitigate AI-generated misinformation. Future research with larger and more diverse cohorts could provide deeper insights into the influence of gender, age, and experience on AI utilization in healthcare. Integrating gender-based interaction differences into AI training and applications may improve clinical performance and promote more equitable healthcare practices.

Authors 6

1. Patricia Mashburn Aachen Institute of Occupational, Social and Environmental Medicine Medical Faculty

   RWTH Aachen University

   Affiliation as printed

   Institute of Occupational, Social and Environmental Medicine, Medical Faculty, RWTH Aachen University, 52074 Aachen, Germany

2. Felix A. Weuthen Aachen Institute of Occupational, Social and Environmental Medicine Medical Faculty

   RWTH Aachen University

   Affiliation as printed

   Institute of Occupational, Social and Environmental Medicine, Medical Faculty, RWTH Aachen University, 52074 Aachen, Germany

3. Nelly Otte Aachen Institute of Occupational, Social and Environmental Medicine Medical Faculty

   RWTH Aachen University

   Affiliation as printed

   Institute of Occupational, Social and Environmental Medicine, Medical Faculty, RWTH Aachen University, 52074 Aachen, Germany

4. Hanif Krabbe

   Ruhr University Bochum · St. Josef-Hospital · Katholisches Klinikum Bochum

   Affiliation as printed

   Department of Vascular Surgery, St. Josef Hospital Bochum, Katholisches Klinikum Bochum, Medical Faculty, Ruhr University Bochum, 44791 Bochum, Germany

5. Thomas Kraus Aachen Institute of Occupational, Social and Environmental Medicine Medical Faculty

   RWTH Aachen University

   Affiliation as printed

   Institute of Occupational, Social and Environmental Medicine, Medical Faculty, RWTH Aachen University, 52074 Aachen, Germany

6. Julia Krabbe corresponding Aachen Institute of Occupational, Social and Environmental Medicine Medical Faculty

   RWTH Aachen University · Ruhr University Bochum · Institute for Prevention and Occupational Medicine

   Affiliation as printed

   Institute for Prevention and Occupational Medicine of the German Social Accident Insurance, Institute of the Ruhr University Bochum (IPA), Medical Faculty, Ruhr University Bochum, 44789 Bochum, Germany

   Institute of Occupational, Social and Environmental Medicine, Medical Faculty, RWTH Aachen University, 52074 Aachen, Germany