Generative AI · Education leadership

Actionable companion · Assessment and evidence of learning

From evidence to district action: Gender Differences in the Use of ChatGPT as Generative Artificial Intelligence for Clinical Research and Decision-Making in Occupational Medicine

AI-assisted editorial implementation guide. The district actions are proposed applications, not findings or recommendations attributed to the original authors. No additional peer review is asserted.

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Evidence and context

Health-professions education context: test transfer to K–12 rather than assuming it.

Original authors’ abstract, reproduced under the linked source license:

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.

A 90-day district pilot

  1. Days 1–15 · Appraise. Have the curriculum lead and a teacher read the methods and limitations. Record the study design, sample, comparator, outcomes and evidence gaps. Define a local question that the study can help inform.
  2. Days 16–30 · Design. Redesign one low-stakes assessment: require a first attempt, an AI-assisted revision, a source-check log and a short oral explanation. Use the same rubric for the comparison task. Name an accountable owner and set the baseline, success threshold and budget before implementation.
  3. Days 31–60 · Test. Start with a small voluntary cohort, preserve an accessible non-AI route and require teacher review. Use synthetic or de-identified examples; approve data handling and tool terms through district processes.
  4. Days 61–90 · Decide. Blind-score reasoning, factual accuracy and unaided transfer on a fresh task. Record suspected misconduct separately from demonstrated evidence. Compare with baseline, document uncertainty and stop or revise if learning worsens, access is unequal or critical errors persist.

The board decision brief

Report the learning problem, evidence quality, participating population, total cost including review time, measured benefit, errors, access differences and remaining uncertainties. Make a dated recommendation to stop, revise or expand. A positive perception survey alone does not establish learning improvement.

Attribution and reuse

Patricia Mashburn; Felix A. Weuthen; Nelly Otte; Hanif Krabbe; Gerardo M. Fernandez; Thomas Kraus; Julia Krabbe. Gender Differences in the Use of ChatGPT as Generative Artificial Intelligence for Clinical Research and Decision-Making in Occupational Medicine. Healthcare (2025-06-11). https://doi.org/10.3390/healthcare13121394

Companion prepared 2026-10-30. Original authorship and DOI are preserved. The abstract is an attributed source extract; the implementation plan is new AI-assisted editorial material, offered under the same CC BY 4.0 license. This guide has not undergone independent peer review.