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Large Language Models

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Evaluation of Large Language Models in Tailoring Educational Content for Cancer Survivors and Their Caregivers: Quality Analysis.

JMIR cancer
BACKGROUND: Cancer survivors and their caregivers, particularly those from disadvantaged backgrounds with limited health literacy or racial and ethnic minorities facing language barriers, are at a disproportionately higher risk of experiencing sympto...

Personalized glucose forecasting for people with type 1 diabetes using large language models.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Type 1 Diabetes (T1D) is an autoimmune disease that requires exogenous insulin via Multiple Daily Injections (MDIs) or subcutaneous pumps to maintain targeted glucose levels. Despite the advances in Continuous Glucose Monito...

Benchmarking large language models for biomedical natural language processing applications and recommendations.

Nature communications
The rapid growth of biomedical literature poses challenges for manual knowledge curation and synthesis. Biomedical Natural Language Processing (BioNLP) automates the process. While Large Language Models (LLMs) have shown promise in general domains, t...

Comparing large Language models and human annotators in latent content analysis of sentiment, political leaning, emotional intensity and sarcasm.

Scientific reports
In the era of rapid digital communication, vast amounts of textual data are generated daily, demanding efficient methods for latent content analysis to extract meaningful insights. Large Language Models (LLMs) offer potential for automating this proc...

Using large language models as decision support tools in emergency ophthalmology.

International journal of medical informatics
BACKGROUND: Large language models (LLMs) have shown promise in various medical applications, but their potential as decision support tools in emergency ophthalmology remains unevaluated using real-world cases.

Dermacen analytica: A novel methodology integrating multi-modal large language models with machine learning in dermatology.

International journal of medical informatics
OBJECTIVE: To design, implement, evaluate, and quantify a novel and adaptable Artificial Intelligence-empowered methodology aimed at supporting a dermatologist's workflow in assessing and diagnosing skin conditions, leveraging AI's deep image analyti...

AI as a decision support tool in forensic image analysis: A pilot study on integrating large language models into crime scene investigation workflows.

Journal of forensic sciences
This study evaluates the effectiveness of artificial intelligence (AI) tools (ChatGPT-4, Claude, and Gemini) in forensic image analysis of crime scenes, marking a significant step toward developing bespoke AI models for forensic applications. The res...

Enhancing data quality in medical concept normalization through large language models.

Journal of biomedical informatics
OBJECTIVE: Medical concept normalization (MCN) aims to map informal medical terms to formal medical concepts, a critical task in building machine learning systems for medical applications. However, most existing studies on MCN primarily focus on mode...

Large Language Models in Summarizing Radiology Report Impressions for Lung Cancer in Chinese: Evaluation Study.

Journal of medical Internet research
BACKGROUND: Large language models (LLMs), such as ChatGPT, have demonstrated impressive capabilities in various natural language processing tasks, particularly in text generation. However, their effectiveness in summarizing radiology report impressio...

DALL-M: Context-aware clinical data augmentation with large language models.

Computers in biology and medicine
X-ray images are vital in medical diagnostics, but their effectiveness is limited without clinical context. Radiologists often find chest X-rays insufficient for diagnosing underlying diseases, necessitating the integration of structured clinical fea...