Analyzing Diversity in Healthcare LLM Research: A Scientometric Perspective
It addresses the problem of underrepresentation and bias in healthcare LLM development, which can lead to inequitable healthcare delivery, by analyzing diversity and proposing strategies for inclusivity.
This paper conducted a scientometric analysis of LLM research in healthcare from 2021 to 2024, revealing significant gender and geographic disparities, such as a predominance of male authors and contributions from high-income countries, and introduced a novel journal diversity index to measure inclusiveness.
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 July 1, 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 diversity 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.