# Analyzing Diversity in Healthcare LLM Research: A Scientometric Perspective

arXiv: https://arxiv.org/abs/2406.13152 · HTML: https://mlsanity.com/paper/2406.13152/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 36 references that are in the map.
- First posted on arXiv 19 Jun 2024.

## Builds on (most cited first, 9 of 9)

- [Language (Technology) is Power: A Critical Survey of "Bias" in NLP](https://mlsanity.com/paper/2005.14050/) (2020)
- ["I'm sorry to hear that": Finding New Biases in Language Models with a Holistic Descriptor Dataset](https://mlsanity.com/paper/2205.09209/) (2022)
- [Summary of ChatGPT-Related Research and Perspective Towards the Future of Large Language Models](https://mlsanity.com/paper/2304.01852/) (2023)
- [Natural Language Processing: State of The Art, Current Trends and Challenges](https://mlsanity.com/paper/1708.05148/) (2017)
- [Generative Text-Guided 3D Vision-Language Pretraining for Unified Medical Image Segmentation](https://mlsanity.com/paper/2306.04811/) (2023)
- [Efficiency optimization of large-scale language models based on deep learning in natural language processing tasks](https://mlsanity.com/paper/2405.11704/) (2024)
- [Exploration of Attention Mechanism-Enhanced Deep Learning Models in the Mining of Medical Textual Data](https://mlsanity.com/paper/2406.00016/) (2024)
- [Integrating Medical Imaging and Clinical Reports Using Multimodal Deep Learning for Advanced Disease Analysis](https://mlsanity.com/paper/2405.17459/) (2024)
- [A Review of Theory and Practice in Scientometrics](https://mlsanity.com/paper/1501.05462/) (2015)

## Built on by (most cited first, 4 of 4)

- [Lived Experience Not Found: LLMs Struggle to Align with Experts on Addressing Adverse Drug Reactions from Psychiatric Medication Use](https://mlsanity.com/paper/2410.19155/) (2024)
- [Breaking the Loop: Detecting and Mitigating Denial-of-Service Vulnerabilities in Large Language Models](https://mlsanity.com/paper/2503.00416/) (2025)
- [Artificial Intelligence for Public Health Surveillance in Africa: Applications and Opportunities](https://mlsanity.com/paper/2408.02575/) (2024)
- [Self-Supervised Learning for Graph-Structured Data in Healthcare Applications: A Comprehensive Review](https://mlsanity.com/paper/2412.05312/) (2024)

## Abstract (arXiv)

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.