{
  "abstract": "Acute flaccid paralysis (AFP) surveillance in Ethiopia remains limited due to hard-to-reach populations, restricted healthcare access, low public awareness, and persistent underreporting within community-based surveillance (CBS) systems. Contributing to this are inadequate training of community reporters, communication barriers, and cultural beliefs and misinformation that further hinder timely case detection. Here, AFP Assistant, a multilingual large language model powered chatbot, is developed to strengthen CBS through improved health communication and real-time reporting. The system supports Amharic, Afaan Oromo, and English to enhance accessibility and inclusivity. A curated dataset derived from literature, guidelines, and community interviews was translated and validated by experts to ensure clinical and cultural accuracy. The system integrates supervised fine-tuning with retrieval-augmented generation (RAG) to deliver accurate, context-aware responses. Trained on 468 question–answer pairs using pretrained Gemini 2.5-flash, the model achieved an accuracy of 0.90 and a loss of 0.31. The RAG framework improved retrieval relevance, grounding, and response efficiency. Evaluation through in-house testing and human annotation using a structured rubric demonstrated robust performance across tasks and languages, while user feedback confirmed usability and relevance. These findings highlight the potential of AI-driven chatbots to enhance AFP surveillance and equitable health communication in low-resource settings.",
  "abstract_shown": true,
  "affiliations": [
    "School of Biomedical Engineering, Jimma Institute of Technology, Jimma University",
    "Faculty of Computing and Informatics, Jimma Institute of Technology, Jimma University",
    "Department of Educational Technology and Information Management, Mattu University",
    "Department of Biomedical Engineering, Gannon University",
    "Global South Artificial Intelligence for Pandemic and Epidemic Preparedness and Response Network (AI4PEP)"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 11,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Gelane Biru"
    },
    {
      "affs": [
        0
      ],
      "name": "Honey Gemechu"
    },
    {
      "affs": [
        0
      ],
      "name": "Firanol Teshome"
    },
    {
      "affs": [
        0
      ],
      "name": "Eyerusalem Gebremeskel"
    },
    {
      "affs": [
        0
      ],
      "name": "Hundessa Daba Nemomssa"
    },
    {
      "affs": [
        0
      ],
      "name": "Kokeb Dese"
    },
    {
      "affs": [
        1
      ],
      "name": "Melaku Tilahun"
    },
    {
      "affs": [
        2
      ],
      "name": "Karrasaaqii Bulti Wakgari"
    },
    {
      "affs": [
        3
      ],
      "name": "Nardos Bisrat"
    },
    {
      "affs": [
        0
      ],
      "name": "Gelan Ayana"
    },
    {
      "affs": [
        4
      ],
      "name": "Jude Kong"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/29xzstyjrsftv.bib",
  "cited_by": 1,
  "doi": "10.1038/s41746-026-02944-4",
  "fulltext_url": "https://doi.org/10.1038/s41746-026-02944-4",
  "id": "29xzstyjrsftv",
  "issns": [
    "2398-6352"
  ],
  "landing_url": "https://doi.org/10.1038/s41746-026-02944-4",
  "language": "en",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "lcc",
      "R858-859.7"
    ],
    [
      "openalex",
      "2718|Artificial Intelligence in Healthcare and Education"
    ]
  ],
  "openalex": "W7166728255",
  "pdf_url": "https://www.nature.com/articles/s41746-026-02944-4_reference.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-06-01",
  "publisher": "Nature Portfolio",
  "ris": "https://research.draconitic.com/paper/29xzstyjrsftv.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T13:46:26Z",
      "native_id": "c5ce6881605d4e76b1fdb3ab4109be79",
      "source": "doaj",
      "url": "https://doi.org/10.1038/s41746-026-02944-4"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "openalex:2718|Artificial Intelligence in Healthcare and Education",
      "subfield": "epidemiology-public-health"
    }
  ],
  "title": "AFP assistant: a retrieval-augmented generation and large language model-powered multilingual polio chatbot for low-resource language communities",
  "title_text": "AFP assistant: a retrieval-augmented generation and large language model-powered multilingual polio chatbot for low-resource language communities",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T13:46:26Z",
  "url": "https://research.draconitic.com/paper/29xzstyjrsftv",
  "venue": "npj Digital Medicine",
  "versions": []
}