  The deployment of large language models (LLMs) in healthcare has demonstrated
substantial potential for enhancing clinical decision-making, administrative
efficiency, and patient outcomes. However, the underrepresentation of diverse
groups in the development and application of these models can perpetuate
biases, leading to inequitable healthcare delivery. This paper presents a
comprehensive scientometric analysis of LLM research for healthcare, including
data from January 1, 2021, to June 16, 2024. By analyzing metadata from PubMed
and Dimensions, including author affiliations, countries, and funding sources,
we assess the diversity of contributors to LLM research. Our findings highlight
significant gender and geographic disparities, with a predominance of male
authors and contributions primarily from high-income countries (HICs). We
introduce a novel journal diversity index based on Gini impurity to measure the
inclusiveness of scientific publications. Our results underscore the necessity
for greater representation in order to ensure the equitable application of LLMs
in healthcare. We propose actionable strategies to enhance diversity and
inclusivity in artificial intelligence research, with the ultimate goal of
fostering a more inclusive and equitable future in healthcare innovation.
