BACKGROUND: Large language models (LLMs) can generate outputs understandable by humans, such as answers to medical questions and radiology reports. With the rapid development of LLMs, clinicians face a growing challenge in determining the most suitab...
BACKGROUND: Accurately predicting children's memory and suggestibility in forensic contexts, such as child sexual abuse (CSA) investigations, remains challenging for human professionals. Large Language Model (LLM), as an advanced natural language pro...
Emotional intelligence is essential for high-stakes interactions in the perioperative setting. Whether addressing patient concerns, resolving conflicts, or triaging cases, anesthesiologists rely on emotional intelligence for effective communication. ...
Large language models (LLMs) have demonstrated remarkable potential in medical applications. However, they still face critical challenges such as hallucinations, knowledge inconsistency, and insufficient integration of domain-specific medical experti...
BACKGROUND: Disease name recognition is a fundamental task in clinical natural language processing, enabling the extraction of critical patient information from electronic health records. While recent advances in large language models (LLMs) have sho...
BACKGROUND: Recent advancements in large language models (LLMs) have generated significant interest in their potential for assessing psychological constructs, particularly personality traits. While prior research has explored LLMs' capabilities in ze...
The substantial increase in mental health disorders globally necessitates scalable, accurate tools for detecting and classifying these conditions in digital environments. This study addresses the critical challenge of automated mental health classifi...
RATIONALE AND OBJECTIVES: Large Language Models (LLMs) show promise for generating patient-friendly radiology reports, but the performance of open-source versus proprietary LLMs needs assessment. To compare open-source and proprietary LLMs in generat...
We present a comprehensive map of the metabolomics research landscape, synthesizing insights from over 80,000 publications. Using PubMedBERT, we transformed abstracts into 768-dimensional embeddings that capture the nuanced thematic structure of the ...
In recent years, Transformer-based large language models (LLMs) have significantly improved upon their text generation capability. Mental health is a serious concern that can be addressed using LLM-based automated mental health counselors. These syst...
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