ArticlePLOS digital health2026
A Bi-lingual chatbot implementation for pandemic response using the transformer-based approach.
Article in PLOS digital health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The COVID-19 pandemic highlighted the critical need for timely and accurate information in effectively managing pandemics. The proliferation of misinformation on public media platforms complicated the management of the pandemic, necessitating a large and constant human resource to meet the demand for trustworthy information. To address this challenge, we developed an intelligent bilingual chatbot that is available 24/7 to provide medically curated up-to-date and approved pandemic management information in English and Luganda. This approach leveraged deep learning to train a chatbot on a growing corpus of curated pandemic-specific information, questions, and answers. Our results demonstrate an implementation of a chatbot that leverages the well-resourced English NLP framework to enable chatting in the Luganda language. They also show that the chatbot is an effective and flexible tool for disseminating accurate information in real-time, while also providing opportunities for continuous improvement through conversation-driven development.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.