AIMC Topic: Information Storage and Retrieval

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Artificial intelligence for solving pediatric clinical cases: A Retrieval-Augmented approach utilizing Llama3.2 and structured references.

International journal of medical informatics
BACKGROUND: The "hallucinations" of Large Language Models (LLMs) raise concerns about their accuracy in pediatrics. This study aimed to evaluate whether integrating information from the Nelson Textbook of Pediatrics through a Retrieval-Augmented Gene...

Unified semantic space learning for cross-modal retrieval.

Neural networks : the official journal of the International Neural Network Society
With the increasing amount of multimodal data on the Internet, cross-modal retrieval has gradually become a hot research topic and has achieved significant progress, especially since graph convolutional networks were introduced. Most methods based on...

Evaluating large language models for information extraction from gastroscopy and colonoscopy reports through multi-strategy prompting.

Journal of biomedical informatics
OBJECTIVE: To systematically evaluate large language models (LLMs) for automated information extraction from gastroscopy and colonoscopy reports through prompt engineering, addressing their ability to extract structured information, recognize complex...

Streamlining systematic reviews with large language models using prompt engineering and retrieval augmented generation.

BMC medical research methodology
BACKGROUND: Systematic reviews (SRs) are essential to formulate evidence-based guidelines but require time-consuming and costly literature screening. Large Language Models (LLMs) can be a powerful tool to expedite SRs.

Use of Retrieval-Augmented Large Language Model for COVID-19 Fact-Checking: Development and Usability Study.

Journal of medical Internet research
BACKGROUND: The COVID-19 pandemic has been accompanied by an "infodemic," where the rapid spread of misinformation has exacerbated public health challenges. Traditional fact-checking methods, though effective, are time-consuming and resource-intensiv...

Using Large Language Models to Automate Data Extraction From Surgical Pathology Reports: Retrospective Cohort Study.

JMIR formative research
BACKGROUND: Popularized by ChatGPT, large language models (LLMs) are poised to transform the scalability of clinical natural language processing (NLP) downstream tasks such as medical question answering (MQA) and automated data extraction from clinic...

Using artificial intelligence tools to automate data extraction for living evidence syntheses.

PloS one
Living evidence synthesis (LES) involves repeatedly updating a systematic review or meta-analysis at regular intervals to incorporate new evidence into the summary results. It requires a considerable amount of human time investment in the article sea...

Revisiting medical image retrieval via knowledge consolidation.

Medical image analysis
As artificial intelligence and digital medicine increasingly permeate healthcare systems, robust governance frameworks are essential to ensure ethical, secure, and effective implementation. In this context, medical image retrieval becomes a critical ...

Improving Dietary Supplement Information Retrieval: Development of a Retrieval-Augmented Generation System With Large Language Models.

Journal of medical Internet research
BACKGROUND: Dietary supplements (DSs) are widely used to improve health and nutrition, but challenges related to misinformation, safety, and efficacy persist due to less stringent regulations compared with pharmaceuticals. Accurate and reliable DS in...

Topicwise Separable Sentence Retrieval for Medical Report Generation.

IEEE transactions on medical imaging
Automated radiology reporting holds immense clinical potential in alleviating the burdensome workload of radiologists and mitigating diagnostic bias. Recently, retrieval-based report generation methods have garnered increasing attention. These method...