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Workshop: Generative AI for Education (GAIED): Advances, Opportunities, and Challenges

Paper 30: Transforming Healthcare Education: Harnessing Large Language Models for Frontline Health Worker Capacity Building using Retrieval-Augmented Generation

Yasmina Al Ghadban · Huiqi Yvonne Lu · Uday Adavi · Ankita · Sridevi Gara · Neelanjana Das · Bhaskar Kumar · Renu Johns · Praveen Devarsetty · Jane Hirst · Uday Adavi

Keywords: [ Medical Education ] [ Embedding Retrieval ] [ Large language models ] [ Retrieval Augmented Generation ]


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, SMARThealth GPT, 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.

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