AIMC Topic: Large Language Models

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Machine translationese of large language models: Dependency triplets, text classification, and SHAP analysis.

PloS one
This study addresses the challenge of distinguishing human translations from those generated by Large Language Models (LLMs) by utilizing dependency triplet features and evaluating 16 machine learning classifiers. Using 10-fold cross-validation, the ...

Automated Safety Plan Scoring in Outpatient Mental Health Settings Using Large Language Models: Exploratory Study.

JMIR mental health
BACKGROUND: The safety planning intervention (SPI) is a suicide prevention intervention that results in a written plan to help patients reduce suicide risk. High-quality safety plans-that is, those that are the most complete, personalized, and specif...

Intervention in Health Misinformation Using Large Language Models for Automated Detection, Thematic Analysis, and Inoculation: Case Study on COVID-19.

Journal of medical Internet research
BACKGROUND: The rapid growth of social media as an information channel has enabled the swift spread of inaccurate or false health information, significantly impacting public health. This widespread dissemination of misinformation has caused confusion...

Reliability of Large Language Model Generated Clinical Reasoning in Assisted Reproductive Technology: Blinded Comparative Evaluation Study.

Journal of medical Internet research
BACKGROUND: High-quality clinical chains-of-thought (CoTs) are essential for explainable medical artificial intelligence (AI); yet, their development is limited by data scarcity. Large language models can generate medical CoTs, but their clinical rel...

Large Language Models in Patient Health Communication for Atherosclerotic Cardiovascular Disease: Pilot Cross-Sectional Comparative Analysis.

JMIR medical informatics
BACKGROUND: Large language models (LLMs) have emerged as promising tools for enhancing public access to medical information, particularly for chronic diseases such as atherosclerotic cardiovascular disease (ASCVD). However, their effectiveness in pat...

Assessing the Impact of the Quality of Textual Data on Feature Representation and Machine Learning Models: Quantitative Study Using Large Language Models.

Journal of medical Internet research
BACKGROUND: Data collected in controlled settings typically results in high-quality datasets. However, in real-world applications, the quality of data collection is often compromised. It is well established that the quality of a dataset significantly...

MaterialBrain: High-Performance Material Synthesis Extraction via Human-AI-Curated Few-Shot Large Language Models.

Journal of chemical information and modeling
The extraction of the metal-organic framework synthesis route from the literature has been crucial for the rational MOFs design with desirable functionality. The recent advent of large language models (LLMs) provides a disruptive new solution to this...

Evaluating Multiple Input Strategies of Large Language Models for Gallbladder Polyps on Ultrasound: Comparative Study.

JMIR medical informatics
BACKGROUND: Gallbladder polyps have a high prevalence and are predominantly benign lesions, often detected via ultrasound. They impose diagnostic burdens on radiologists while generating substantial patient demand for report interpretation. Benign po...

Performance of large language models in reporting oral health concerns and side effects in head and neck cancer: a comparative study.

Journal of cancer research and clinical oncology
PURPOSE: With increasing reliance on large language models (LLMs) for health information, this study evaluated reliability and quality, understandability, actionability, readability and misinformation risk of responses from LLMs to oral health concer...

Automated Multitier Tagging of Chinese Online Health Education Resources Using a Large Language Model: Development and Validation Study.

Journal of medical Internet research
BACKGROUND: Precision health promotion, which aims to tailor health messages to individual needs, is hampered by the lack of structured metadata in vast digital health resource libraries. This bottleneck prevents scalable, personalized content delive...