Related Experiment Video
Updated: Jul 2, 2026
03:14
Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
Published on: December 6, 2024
AFP assistant: a retrieval-augmented generation and large language model-powered multilingual polio chatbot for
Gelane Biru1,2, Honey Gemechu1,2, Firanol Teshome1,2
1School of Biomedical Engineering, Jimma Institute of Technology, Jimma University, Jimma, Ethiopia.
NPJ Digital Medicine
|June 30, 2026
Summary
Ethiopia
Area of Science:
- Public Health
- Artificial Intelligence
- Health Communication
Background:
- Acute flaccid paralysis (AFP) surveillance in Ethiopia faces challenges including hard-to-reach populations, limited healthcare access, and underreporting.
- Inadequate training, communication barriers, and cultural factors impede timely case detection in community-based surveillance (CBS).
Purpose of the Study:
- To develop and evaluate AFP Assistant, an AI-powered chatbot, to strengthen CBS in Ethiopia.
- To improve health communication and real-time reporting for AFP surveillance using multilingual support.
Main Methods:
- Developed AFP Assistant, a multilingual chatbot supporting Amharic, Afaan Oromo, and English.
- Utilized supervised fine-tuning with retrieval-augmented generation (RAG) on a curated dataset.
- Trained the model on 468 question-answer pairs using Gemini 2.5-flash.
Main Results:
- The AI model achieved 0.90 accuracy and 0.31 loss.
- RAG framework enhanced retrieval relevance, grounding, and response efficiency.
- In-house testing and human annotation confirmed robust performance and usability across languages.
Conclusions:
- AI-driven chatbots like AFP Assistant can significantly enhance AFP surveillance in low-resource settings.
- The system demonstrates potential for improving equitable health communication and timely case detection.
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