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.

大规模语言模型在医疗保健领域的部署展示了提升临床决策、行政效率和患者成果的巨大潜力。然而，这些模型在开发和应用中存在多样性群体的代表不足，会导致不公平的医疗保健服务。本文通过对2021年1月1日至2024年6月16日期间的PubMed和Dimensions的元数据进行分析，包括作者机构、国家和资金来源，评估了在医疗保健领域的LLM研究的多样性。我们的发现强调了性别和地理差异的重要性，男性作者占主导地位，贡献主要来自高收入国家 (HICs)。我们引入了基于基尼不纯度的新颖期刊多样性指数来衡量科学出版物的包容性。我们的研究结果强调了加强代表性的必要性，以确保在医疗保健中公平应用LLMs。我们提出了行动策略来增强人工智能研究的多样性和包容性，以实现更具包容性和公平性的医疗创新未来。

分析医疗多样性的LLM研究：科学计量学视角