Research on the Application of Large Language Models in the Gout Field in Vietnam: A Localization Exploration of Medical AI

This article focuses on the NtQuang team's research on the application of large language models in Vietnam's gout medical field. It explores the core challenges of medical AI localization (low-resource language support, regional characteristics of diseases), technical paths (multilingual model selection, domain adaptation, instruction fine-tuning), application scenarios, ethical and safety considerations, and global implications, aiming to promote AI technology to meet medical needs in more regions.

Global medical AI is developing rapidly, but mainstream LLMs are mostly trained on English and have weak support for low-resource languages like Vietnamese. Gout has a high incidence in Vietnam, influenced by high-purine diets, beer culture, genetics, and differences in medical resources, leading to regional characteristics in diagnosis and treatment. Vietnamese traditional medicine also has unique perceptions of gout—all these are factors that localized AI needs to consider.

As a tonal language, Vietnamese medical texts contain a large number of loanwords, local terms, and folk names, and medical data is scarce. The research team uses multilingual models such as PhoBERT and XLM-R, and solves problems through domain adaptation (pre-training on Vietnamese medical literature), cross-language transfer (using English medical knowledge bases), and instruction fine-tuning (building a gout task dataset).

This LLM can serve patients (disease popularization, medication guidance), primary care providers (clinical decision support), and researchers (literature review assistance). In particular, it can bridge the gap in medical resources between urban and rural areas in Vietnam, provide professional references for remote regions, and improve the quality of diagnosis and treatment.

It is necessary to ensure information accuracy (built-in fact-checking), cultural sensitivity (respecting Vietnamese traditional eating habits and beliefs), and privacy protection (complying with data regulations, using de-identification and differential privacy technologies) to avoid medical errors and privacy leaks.

This research provides a replicable framework for other developing countries (focusing on local diseases, using transfer learning, etc.). In the future, we can deepen multimodal integration (text + medical imaging), personalized medicine, accumulate Vietnamese medical data to narrow the gap with English AI, and establish a cross-language knowledge sharing mechanism.

The LLM research in Vietnam's gout field is an important attempt at medical AI localization. It emphasizes that technology should benefit all language users and promote the construction of a fair and inclusive medical system through targeted innovation.