The field of medical natural language processing (NLP) is rapidly advancing, with a focus on developing domain-specific language models and improving their performance in low-resource languages and settings. Recent research has highlighted the importance of domain adaptation and cross-lingual transferability in medical NLP, enabling the development of more accurate and reliable models for tasks such as patient screening, named entity recognition, and conversational AI. The use of lightweight and offline-capable language models is also gaining traction, particularly in resource-constrained environments such as rural areas. Furthermore, the creation of high-quality datasets and the application of data filtering techniques are crucial for improving the performance of language models in medical NLP. Notable papers in this area include:

• A study on multilingual BERT language models for medical tasks, which demonstrated the effectiveness of domain adaptation and cross-lingual transferability.
• The introduction of AyurParam, a bilingual language model for Ayurveda, which surpassed other models in its size class and demonstrated the necessity for authentic domain adaptation and high-quality supervision.
• The development of FirstAidQA, a synthetic dataset for first aid and emergency response, which enables the creation of lightweight and offline-capable language models for safety-critical applications.