Transforming Healthcare Education: Harnessing Large Language Models for Frontline Health Worker Capacity Building using Retrieval-Augmented Generation
Transforming Healthcare Education: Harnessing Large Language Models for Frontline Health Worker Capacity Building using Retrieval-Augmented Generation
复制标题
转变医疗保健教育:利用大型语言模型利用检索增强生成技术进行一线卫生工作者的能力建设
DOI:
10.1101/2023.12.15.23300009
复制
发表时间:
2023
期刊:
影响因子:
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通讯作者:
Al Ghadban Y
中科院分区:
文献类型:
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作者:
Al Ghadban Y
In recent years, large language models (LLMs) have emerged as a transformative force in several domains, including medical education and healthcare. This paper presents a case study on the practical application of using retrieval-augmented generation (RAG) based models for enhancing healthcare education in low- and middle-income countries. The model described in this paper, SMARThealthGPT, stems from the necessity for accessible and locally relevant medical information to aid community health workers in delivering high-quality maternal care. We describe the development process of the complete RAG pipeline, including the creation of a knowledge base of Indian pregnancy-related guidelines, knowledge embedding retrieval, parameter selection and optimization, and answer generation. This case study highlights the potential of LLMs in building frontline healthcare worker capacity and enhancing guideline-based health education; and offers insights for similar applications in resource-limited settings. It serves as a reference for machine learning scientists, educators, healthcare professionals, and policymakers aiming to harness the power of LLMs for substantial educational improvement.
DOI:
10.3389/fgwh.2021.620759
发表时间:
2021
期刊:
Frontiers in global women's health
影响因子:
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作者:
Nagraj S;Kennedy SH;Jha V;Norton R;Hinton L;Billot L;Rajan E;Arora V;Praveen D;Hirst JE
通讯作者:
Hirst JE