The integration of large language models (LLMs) in mental health care is an emerging field.
There is a need to systematically review the application outcomes and delineate the …

Privacy and ethical considerations limit access to large-scale clinical datasets, particularly
clinical text data, which contain extensive and diverse information and serve as the …

Recent advancements in large language models (LLMs) have transformed the field of
question answering (QA). However, evaluating LLMs in the medical field is challenging due …

Integrating large language models (LLMs) into healthcare holds great potential but faces
challenges. Pre-training LLMs from scratch for domains like medicine is resource-heavy and …

Large Language Models (LLMs) have introduced a new era of proficiency in comprehending
complex healthcare and biomedical topics. However, there is a noticeable lack of models in …

The recent advances in natural language processing (NLP), have led to a new trend of
applying large language models (LLMs) to real-world scenarios. While the latest LLMs are …

Objective Social media-based public health research is crucial for epidemic surveillance, but
most studies identify relevant corpora with keyword-matching. This study develops a system …

Pre-trained multilingual language models underpin a large portion of modern NLP tools
outside of English. A strong baseline for specializing these models for specific languages is …

Latest instruction-tuned large language models (LLM) show great results on various tasks,
however, they often face performance degradation for non-English input. There is evidence …

Large language models exhibit promising general capabilities but often lack specialized
knowledge for domain-specific tasks. Developing domain experts from a base model …