BACKGROUND: Surgical consent forms convey critical information; yet, their complex language can limit patient comprehension. Large language models (LLMs) can simplify complex information and improve readability, but evidence of the impact of LLM-gene...
BACKGROUND: The opioid crisis poses a significant health challenge in the United States, with increasing overdoses and death rates due to opioids mixed with other illicit substances. Various strategies have been developed by federal and local governm...
BACKGROUND: Large language models (LLMs) can support health care professionals in their daily work, for example, when writing and filing reports or communicating diagnoses. With the rise of LLMs, current research investigates how LLMs could be applie...
Bing Chat (subsequently renamed Microsoft Copilot)-a ChatGPT 4.0-based large language model-demonstrated comparable performance to medical students in answering essay-style concept appraisals, while assessors struggled to differentiate artificial int...
BACKGROUND: Large language models (LLMs) offer significant potential to streamline research workflows and enhance productivity. However, limited data exist on the extent of their adoption within the mental health research community.
In the context of Evidence-Based Practice (EBP), Systematic Reviews (SRs), Meta-Analyses (MAs) and overview of reviews have become cornerstones for the synthesis of research findings. The Preferred Reporting Items for Systematic Reviews and Meta-Anal...
BACKGROUND: The rapid advancements in natural language processing, particularly the development of large language models (LLMs), have opened new avenues for managing complex clinical text data. However, the inherent complexity and specificity of medi...
OBJECTIVE: To systematically evaluate large language models (LLMs) for automated information extraction from gastroscopy and colonoscopy reports through prompt engineering, addressing their ability to extract structured information, recognize complex...
Difficult-to-treat depression (DTD) has been proposed as a broader and more clinically comprehensive perspective on a person's depressive disorder where, despite treatment, they continue to experience significant burden. We sought to develop a tool c...
We present a protein engineering approach to directed evolution with machine learning that integrates a new semi-supervised neural network fitness prediction model, Seq2Fitness, and an innovative optimization algorithm, biphasic annealing for diverse...
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