Empowering PET imaging reporting with retrieval-augmented large language models and reading reports database: a pilot single center study.

Purpose: The potential of Large Language Models (LLMs) in enhancing a variety of natural language tasks in clinical fields includes medical imaging reporting. This pilot study examines the efficacy of a retrieval-augmented generation (RAG) LLM system considering zero-shot learning capability of LLMs, integrated with a comprehensive database of PET reading reports, in improving reference to prior reports and decision making.

Methods: We developed a custom LLM framework with retrieval capabilities, leveraging a database of over 10 years of PET imaging reports from a single center.

Production and application of Paenibacillus polymyxa levan as an antiviral priming agent against cucumber mosaic virus infection in tobacco plants.

This study investigates the application of levan- produced from Paenibacillus polymyxa SG09-12 as an antiviral agent against cucumber mosaic virus (CMV). A high-purity microbial levan was produced and purified using diafiltration. The chemical composition, structure, and functional groups of the levan were characterised using high-performance liquid chromatography (HPLC), nuclear magnetic resonance (NMR), Fourier-transform infrared spectroscopy (FT-IR), and X-ray photoelectron spectroscopy (XPS).

ESCARGOT: An AI Agent Leveraging Large Language Models, Dynamic Graph of Thoughts, and Biomedical Knowledge Graphs for Enhanced Reasoning.

Motivation: LLMs like GPT-4, despite their advancements, often produce hallucinations and struggle with integrating external knowledge effectively. While Retrieval-Augmented Generation (RAG) attempts to address this by incorporating external information, it faces significant challenges such as context length limitations and imprecise vector similarity search. ESCARGOT aims to overcome these issues by combining LLMs with a dynamic Graph of Thoughts and biomedical knowledge graphs, improving output reliability and reducing hallucinations.

Artificial intelligence-enhanced comprehensive assessment of the aortic valve stenosis continuum in echocardiography.

Background: Transthoracic echocardiography (TTE) is the primary modality for diagnosing aortic stenosis (AS), yet it requires skilled operators and can be resource-intensive. We developed and validated an artificial intelligence (AI)-based system for evaluating AS that is effective in both resource-limited and advanced settings.

Methods: We created a dual-pathway AI system for AS evaluation using a nationwide echocardiographic dataset (developmental dataset, n = 8427): 1) a deep learning (DL)-based AS continuum assessment algorithm using limited 2D TTE videos, and 2) automating conventional AS evaluation.

Economic evaluation of mass screening as a strategy for hepatitis C virus elimination in South Korea.

Background: This study examines Hepatitis C virus (HCV) screening scenarios to meet World Health Organization (WHO) elimination targets (incidence ≤5 per 100,000, mortality ≤2 per 100,000) and assesses their timeframes and cost-effectiveness.

Methods: A closed cohort model of Koreans aged 30-79 in 2020 projected HCV incidence and mortality over 20 years. Economic evaluations used a dynamic transmission model, considering prevalent and annual incident cases.