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Analyzing Diversity in Healthcare LLM Research: A Scientometric Perspective
June 21, 2024, 4:41 a.m. | David Restrepo, Chenwei Wu, Constanza V\'asquez-Venegas, Jo\~ao Matos, Jack Gallifant, Luis Filipe
cs.CL updates on arXiv.org arxiv.org
Abstract: 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 …
abstract application arxiv biases clinical cs.cl decision delivery deployment development diverse diversity efficiency healthcare however language language models large language large language models llm llm research llms making paper patient perspective potential research type underrepresentation
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