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Large Language Models

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The Feasibility of Large Language Models in Verbal Comprehension Assessment: Mixed Methods Feasibility Study.

JMIR formative research
BACKGROUND: Cognitive assessment is an important component of applied psychology, but limited access and high costs make these evaluations challenging.

Building an intelligent diabetes Q&A system with knowledge graphs and large language models.

Frontiers in public health
INTRODUCTION: This paper introduces an intelligent question-answering system designed to deliver personalized medical information to diabetic patients. By integrating large language models with knowledge graphs, the system aims to provide more accura...

Assessment of large language models in medical quizzes for clinical chemistry and laboratory management: implications and applications for healthcare artificial intelligence.

Scandinavian journal of clinical and laboratory investigation
Large language models (LLMs) have demonstrated high performance across various fields due to their ability to understand, generate, and manipulate human language. However, their potential in specialized medical domains, such as clinical chemistry and...

Automated identification of incidental hepatic steatosis on Emergency Department imaging using large language models.

Hepatology communications
BACKGROUND: Hepatic steatosis is a precursor to more severe liver disease, increasing morbidity and mortality risks. In the Emergency Department, routine abdominal imaging often reveals incidental hepatic steatosis that goes undiagnosed due to the ac...

Injury degree appraisal of large language model based on retrieval-augmented generation and deep learning.

International journal of law and psychiatry
Large Language Models (LLMs) have shown impressive performance in various natural language processing tasks. However, their application in specialized domains like forensic injury appraisal remains challenging due to the lack of domain-specific knowl...

Supervised machine learning compared to large language models for identifying functional seizures from medical records.

Epilepsia
OBJECTIVE: The Functional Seizures Likelihood Score (FSLS) is a supervised machine learning-based diagnostic score that was developed to differentiate functional seizures (FS) from epileptic seizures (ES). In contrast to this targeted approach, large...

Large Language Models-Supported Thrombectomy Decision-Making in Acute Ischemic Stroke Based on Radiology Reports: Feasibility Qualitative Study.

Journal of medical Internet research
BACKGROUND: The latest advancement of artificial intelligence (AI) is generative pretrained transformer large language models (LLMs). They have been trained on massive amounts of text, enabling humanlike and semantical responses to text-based inputs ...

Table tennis coaching system based on a multimodal large language model with a table tennis knowledge base.

PloS one
UNLABELLED: Table tennis is one of the most popular sports in the world, and it plays a positive role in the overall development of people's physical and mental health. This study develops an AI table tennis coaching system using a Multimodal Large L...

Empowering large language models for automated clinical assessment with generation-augmented retrieval and hierarchical chain-of-thought.

Artificial intelligence in medicine
BACKGROUND: Understanding and extracting valuable information from electronic health records (EHRs) is important for improving healthcare delivery and health outcomes. Large language models (LLMs) have demonstrated significant proficiency in natural ...

The large language model diagnoses tuberculous pleural effusion in pleural effusion patients through clinical feature landscapes.

Respiratory research
BACKGROUND: Tuberculous pleural effusion (TPE) is a challenging extrapulmonary manifestation of tuberculosis, with traditional diagnostic methods often involving invasive surgery and being time-consuming. While various machine learning and statistica...