Evaluating Fine-Tuned and Base Language Models in Maternal and Vaccination Healthcare for African Settings
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Computer Science > Computation and Language
arXiv:2609.22110 (cs)
[Submitted on 17 Aug 2026]
Title:Evaluating Fine-Tuned and Base Language Models in Maternal and Vaccination Healthcare for African Settings
Authors:Abdulquddus Ajibade, Oluwaseun Odunsi, Iyinoluwa Animasaun, Chioma Nwakanma-Akanno, Oluwasegun Oguntuase, Oluwafunke Akinbuwa, Abiodun Adereni
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Abstract:Background: Large language models (LLMs) can improve healthcare information delivery in low-resource settings but may produce inaccurate or culturally inappropriate advice. This study evaluated domain-specific fine-tuning for maternal health and vaccination in Nigeria. Objective: To compare HelpMum's MamaBot-Llama and Vax-Llama with Meta's Llama-3.1-8B-Instruct for accuracy, safety, clarity, contextual appropriateness, and trustworthiness. Methods: We evaluated 200 healthcare questions, 100 each for maternal health and vaccination, across five subdomains per domain. MamaBot-Llama and Vax-Llama were fine-tuned using Low-Rank Adaptation on over 36,000 maternal health and 9,000 vaccination question-answer pairs, respectively. Two Nigerian licensed physicians independently rated responses using a 5-point Likert scale. Paired comparisons used Wilcoxon signed-rank tests. Results: Performance varied by domain. MamaBot-Llama significantly outperformed the base model across all criteria, with a 4.9% overall improvement (p < .001), including gains in clinical trustworthiness (+7%) and medical accuracy (+5%). Critical issues decreased by 50%, and clinicians preferred it in 78% of cases. In contrast, Vax-Llama showed a 5.2% overall decline (p < .001), with critical issues increasing by 192% and safety concerns by 400%. Conclusions: Domain-specific fine-tuning can improve healthcare LLM performance when based on high-quality, clinician-curated data, but may also degrade performance when dataset quality is inadequate. Rigorous domain-specific validation is essential before clinical deployment. Physician evaluators provided informed consent, and chatbot logs were anonymized. Keywords: Large language models; Fine-tuning; Maternal health; Vaccination; Healthcare AI; Low-resource settings; Nigeria; Model evaluation; LoRA; Medical accuracy
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2609.22110 [cs.CL] |
| (or arXiv:2609.22110v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2609.22110
arXiv-issued DOI via DataCite
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Submission history
From: Oluwasegun Oguntuase [view email][v1] Mon, 17 Aug 2026 16:11:28 UTC (1,414 KB)
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