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

• URL: http://arxiv.org/abs/2608.08896v1
• Date: Sun, 09 Aug 2026 20:16:51 GMT
• Title: From Manuals to Maintenance: Fine-Tuning MedGemma for Multi-Modal Imaging System Support in Low-Resource Settings
• Abstract summary: Imaging device downtime is a major barrier to healthcare delivery in low- and middle-income countries (LMICs)<n>We present a multi-modality medical equipment maintenance question-answering (QA) framework.<n>We curated technical manuals from MRI and ultrasound systems to generate the INGENZI_DatasetV1.<n>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.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Empowering Medical Equipment Sustainability in Low-Resource Settings: An AI-Powered Diagnostic and Support Platform for Biomedical Technicians [0.0]
  In low- and middle-income countries (LMICs), a significant proportion of medical diagnostic equipment remains underutilized or non-functional.<n>This research explores the development and validation of an AI-powered support platform designed to assist biomedical technicians in diagnosing and repairing medical devices in real-time.
  arXiv  Detail & Related papers  (2026-01-23T18:39:55Z)
• Uncertainty-Driven Expert Control: Enhancing the Reliability of Medical Vision-Language Models [52.2001050216955]
  Existing methods aim to enhance the performance of Medical Vision Language Model (MedVLM) by adjusting model structure, fine-tuning with high-quality data, or through preference fine-tuning.<n>We propose an expert-in-the-loop framework named Expert-Controlled-Free Guidance (Expert-CFG) to align MedVLM with clinical expertise without additional training.
  arXiv  Detail & Related papers  (2025-07-12T09:03:30Z)
• MedGemma Technical Report [75.88152277443179]
  We introduce MedGemma, a collection of medical vision-language foundation models based on Gemma 3 4B and 27B.<n>MedGemma demonstrates advanced medical understanding and reasoning on images and text.<n>We additionally introduce MedSigLIP, a medically-tuned vision encoder derived from SigLIP.
  arXiv  Detail & Related papers  (2025-07-07T17:01:44Z)
• GEMeX-RMCoT: An Enhanced Med-VQA Dataset for Region-Aware Multimodal Chain-of-Thought Reasoning [60.03671205298294]
  Medical visual question answering aims to support clinical decision-making by enabling models to answer natural language questions based on medical images.<n>Current methods still suffer from limited answer reliability and poor interpretability.<n>This work first proposes a Region-Aware Multimodal Chain-of-Thought dataset, in which the process of producing an answer is preceded by a sequence of intermediate reasoning steps.
  arXiv  Detail & Related papers  (2025-06-22T08:09:58Z)
• MedBookVQA: A Systematic and Comprehensive Medical Benchmark Derived from Open-Access Book [5.318470975871017]
  We present MedBookVQA, a systematic and comprehensive multimodal benchmark derived from open-access medical textbooks.<n>We generate 5,000 clinically relevant questions spanning modality recognition, classification, anatomical identification, symptom diagnosis, and surgical procedures.<n>We evaluate a wide array of MLLMs, including proprietary, open-sourced, medical, and reasoning models, revealing significant performance disparities across task types and model categories.
  arXiv  Detail & Related papers  (2025-06-01T06:28:36Z)
• Lightweight Clinical Decision Support System using QLoRA-Fine-Tuned LLMs and Retrieval-Augmented Generation [0.0]
  This research paper investigates the application of Large Language Models (LLMs) in healthcare.<n>We focus on enhancing medical decision support through Retrieval-Augmented Generation (RAG) integrated with hospital-specific data and fine-tuning using Quantized Low-Rank Adaptation (QLoRA)<n>We touch on the ethical considerations-patient privacy, data security, and the need for rigorous clinical validation-as well as the practical challenges of integrating such systems into real-world healthcare.
  arXiv  Detail & Related papers  (2025-05-06T10:31:54Z)
• A Method for the Architecture of a Medical Vertical Large Language Model Based on Deepseek R1 [6.589206192038366]
  This paper presents an efficient lightweight medical large language model architecture that addresses knowledge acquisition, model compression, and computational enhancement challenges.<n>We design a knowledge transfer pipeline from DeepSeek-R1-Distill-70B to DeepSeek-R1-Distill-7B using Low-Rank Adaptation (LoRA) for precise medical knowledge retention.<n>Our approach maintains 92.1% accuracy on USMLE while reducing memory consumption by 64.7% and latency by 12.4% compared to baseline inference models.
  arXiv  Detail & Related papers  (2025-04-25T14:28:29Z)
• LLM-MedQA: Enhancing Medical Question Answering through Case Studies in Large Language Models [18.6994780408699]
  Large Language Models (LLMs) face significant challenges in medical question answering.<n>We propose a novel approach incorporating similar case generation within a multi-agent medical question-answering system.<n>Our method capitalizes on the model's inherent medical knowledge and reasoning capabilities, eliminating the need for additional training data.
  arXiv  Detail & Related papers  (2024-12-31T19:55:45Z)
• Comprehensive and Practical Evaluation of Retrieval-Augmented Generation Systems for Medical Question Answering [70.44269982045415]
  Retrieval-augmented generation (RAG) has emerged as a promising approach to enhance the performance of large language models (LLMs) We introduce Medical Retrieval-Augmented Generation Benchmark (MedRGB) that provides various supplementary elements to four medical QA datasets. Our experimental results reveals current models' limited ability to handle noise and misinformation in the retrieved documents.
  arXiv  Detail & Related papers  (2024-11-14T06:19:18Z)
• Towards a clinically accessible radiology foundation model: open-access and lightweight, with automated evaluation [113.5002649181103]
  Training open-source small multimodal models (SMMs) to bridge competency gaps for unmet clinical needs in radiology. For training, we assemble a large dataset of over 697 thousand radiology image-text pairs. For evaluation, we propose CheXprompt, a GPT-4-based metric for factuality evaluation, and demonstrate its parity with expert evaluation. The inference of LlaVA-Rad is fast and can be performed on a single V100 GPU in private settings, offering a promising state-of-the-art tool for real-world clinical applications.
  arXiv  Detail & Related papers  (2024-03-12T18:12:02Z)
• Automated Medical Coding on MIMIC-III and MIMIC-IV: A Critical Review and Replicability Study [60.56194508762205]
  We reproduce, compare, and analyze state-of-the-art automated medical coding machine learning models. We show that several models underperform due to weak configurations, poorly sampled train-test splits, and insufficient evaluation. We present the first comprehensive results on the newly released MIMIC-IV dataset using the reproduced models.
  arXiv  Detail & Related papers  (2023-04-21T11:54:44Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.