BACKGROUND: In the era of data and intelligence, artificial intelligence has been widely applied in the medical field. As the most cutting-edge technology, the large language model (LLM) has gained popularity due to its extraordinary ability to handl...
Recent advances in artificial intelligence have propelled Large Language Models (LLMs) in natural language understanding, enabling new healthcare applications. While LLMs can analyze health data, directly predicting patient risk scores can be unrelia...
BACKGROUND: Chronic diseases present significant challenges in health care, requiring effective management to reduce morbidity and mortality. While digital technologies like wearable devices and mobile applications have been widely adopted, large lan...
This study compared language models' responses to open-ended questions on regenerative therapy guidelines for chronic pain, assessing their accuracy, reliability, usefulness, readability, semantic similarity, and hallucination rates. This cross-secti...
BACKGROUND: Chatbots based on large language models (LLMs) have shown promise in evaluating the consistency of research. Previously, researchers used LLM to assess if randomized controlled trial (RCT) abstracts adhered to the CONSORT-Abstract guideli...
In the evolving field of neurological healthcare, deep learning technologies are gaining recognition for their potential to enhance diagnostic accuracy. Transformer-based models, particularly large language models (LLMs) such as OpenBioLLM, have show...
BACKGROUND: Qualitative research provides essential insights into human behaviors, perceptions, and experiences in health sciences. The COREQ (Consolidated Criteria for Reporting Qualitative Research) checklist, published in 2007 and endorsed by the ...
To evaluate the effectiveness of open-weight large language models (LLMs) in extracting key radiological features and determining National Comprehensive Cancer Network (NCCN) resectability status from free-text radiology reports for pancreatic ducta...
BACKGROUND: Large language model (LLM) fine-tuning is the process of adjusting out-of-the-box model weights using a dataset of interest. Fine-tuning can be a powerful technique to improve model performance in fields like medicine, where LLMs may have...
BACKGROUND: Large language models (LLMs) hold promise for supporting clinical tasks, particularly in data-driven and technical disciplines such as radiation oncology. While prior evaluation studies have focused on examination-style settings for evalu...
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