AIMC Topic: Large Language Models

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Large Language Model-Assisted Risk-of-Bias Assessment in Randomized Controlled Trials Using the Revised Risk-of-Bias Tool: Usability Study.

Journal of medical Internet research
BACKGROUND: The revised Risk-of-Bias tool (RoB2) overcomes the limitations of its predecessor but introduces new implementation challenges. Studies demonstrate low interrater reliability and substantial time requirements for RoB2 implementation. Larg...

[Large language models in healthcare].

Nederlands tijdschrift voor geneeskunde
Large language models, AI-based models that can generate language and which are optimized for human interaction, are widely available and also offer opportunities for healthcare. Yet, they also raise important questions about their quality and ethica...

Extracting critical clinical indicators and survival prediction of lung cancer from pathology reports using large language models.

Computers in biology and medicine
Lung cancer remains the leading cause of cancer deaths in many developed countries, primarily due to late-stage diagnosis. Histopathology, the gold standard for diagnosis, often results in semi-structured pathological reports containing complex infor...

Large language models and women's health: a digital companion for informed decision-making.

Archives of gynecology and obstetrics
The integration of artificial intelligence (AI), particularly large language models (LLMs) like ChatGPT, in gynecology and obstetrics has the potential to significantly transform patient care. These AI-driven tools provide continuous access to inform...

Zero- and few-shot Named Entity Recognition and Text Expansion in medication prescriptions using large language models.

Artificial intelligence in medicine
Medication prescriptions in electronic health records (EHR) are often in free-text and may include a mix of languages, local brand names, and a wide range of idiosyncratic formats and abbreviations. Large language models (LLMs) have shown a promising...

A comparative study of recent large language models on generating hospital discharge summaries for lung cancer patients.

Journal of biomedical informatics
OBJECTIVE: Generating discharge summaries is a crucial yet time-consuming task in clinical practice, essential for conveying pertinent patient information and facilitating continuity of care. Recent advancements in large language models (LLMs) have s...

Assessing large language models for acute heart failure classification and information extraction from French clinical notes.

Computers in biology and medicine
Understanding acute heart failure (AHF) remains a significant challenge, as many clinical details are recorded in unstructured text rather than structured data in electronic health records (EHRs). In this study, we explored the use of large language ...

Ontology enrichment using a large language model: Applying lexical, semantic, and knowledge network-based similarity for concept placement.

Journal of biomedical informatics
OBJECTIVE: Ontologies are essential for representing the knowledge of a domain. To make ontologies useful, they must encompass a comprehensive domain view. To achieve ontology enrichment, there is a need to discover new concepts to be added, either b...

Large Language Model-Assisted Surgical Consent Forms in Non-English Language: Content Analysis and Readability Evaluation.

Journal of medical Internet research
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...

Sentiment Analysis Using a Large Language Model-Based Approach to Detect Opioids Mixed With Other Substances Via Social Media: Method Development and Validation.

JMIR infodemiology
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...