From Manuals to Maintenance: Fine-Tuning MedGemma for Multi-Modal Imaging System Support in Low-Resource Settings

Bernes Lorier Atabonfack; Zion Kongbi Nfo; Ahmed Tahiru Issah; Tolulope Olusuyi; Clemence Ingabire; Mohammed Hardi Abdul Baaki; Mawuli Deku; Abdulrazaq Zubair; Alyasaa Anas; Raymond Confidence; Maruf Adewole; Udunna C. Anazodo

arXiv · 2608.08896

From Manuals to Maintenance: Fine-Tuning MedGemma for Multi-Modal Imaging System Support in Low-Resource Settings

Bernes Lorier Atabonfack·Zion Kongbi Nfo·Ahmed Tahiru Issah·Tolulope Olusuyi·Clemence Ingabire·Mohammed Hardi Abdul Baaki·Mawuli Deku·Abdulrazaq Zubair·Alyasaa Anas·Raymond Confidence·Maruf Adewole·Udunna C. Anazodo

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Abstract

Imaging device downtime is a major barrier to healthcare delivery in low- and middle-income countries (LMICs), often driven by limited access to specialized biomedical engineering support. We present a multi-modality medical equipment maintenance question-answering (QA) framework and demonstrate the fine-tuning of a medical foundation model for specialized technical troubleshooting tasks. Guided by a multi-country survey across nine LMICs, we curated technical manuals from MRI and ultrasound systems to generate the INGENZI_DatasetV1, containing 10,294 high-quality, filtered QA-context pairs. Using QLoRA-based parameter-efficient fine-tuning, we adapted the MedGemma-4b-it model to interpret system error logs and generate step-by-step equipment repair instructions. Compared to the baseline model, the fine-tuned system achieved substantial improvements across metrics, including F1 score (0.22 to 0.38), ROUGE-2 (0.18 to 0.41), and BERTScore F1 (0.86 to 0.91). These metric gains demonstrate that the model generates significantly more precise and procedurally accurate technical responses to new troubleshooting queries. This work establishes a reliable foundation for AI-assisted diagnostic and maintenance tools in resource-constrained settings.

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Bernes Lorier Atabonfack, Zion Kongbi Nfo, Ahmed Tahiru Issah, Tolulope Olusuyi, Clemence Ingabire, Mohammed Hardi Abdul Baaki, Mawuli Deku, Abdulrazaq Zubair, Alyasaa Anas, Raymond Confidence, Maruf Adewole, Udunna C. Anazodo. 2026-08-09. From Manuals to Maintenance: Fine-Tuning MedGemma for Multi-Modal Imaging System Support in Low-Resource Settings. https://arxiv.org/abs/2608.08896

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