AIMC Topic: Large Language Models

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Large language models as an academic resource for radiologists stepping into artificial intelligence research.

Current problems in diagnostic radiology
BACKGROUND: Radiologists increasingly use artificial intelligence (AI) to enhance diagnostic accuracy and optimize workflows. However, many lack the technical skills to effectively apply machine learning (ML) and deep learning (DL) algorithms, limiti...

Intelligent Agent Planning for Optimizing Parallel MRI Reconstruction via A Large Language Model.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Parallel magnetic resonance imaging (pMRI) reconstruction needs tedious parameter tuning process for achieving optimal image quality. Although data-driven artificial intelligence (AI) has significantly improved pMRI reconstruction, knowledge-driven A...

Large Language Models Struggle in Token-Level Clinical Named Entity Recognition.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Large Language Models (LLMs) have revolutionized various sectors, including healthcare where they are employed in diverse applications. Their utility is particularly significant in the context of rare diseases, where data scarcity, complexity, and sp...

Evaluating the Performance of Large Language Models for Named Entity Recognition in Ophthalmology Clinical Free-Text Notes.

AMIA ... Annual Symposium proceedings. AMIA Symposium
This study compared large language models (LLMs) and Bidirectional Encoder Representations from Transformers (BERT) models in identifying medication names, routes, and frequencies from publicly available free-text ophthalmology progress notes of 480 ...

Optimizing Large Language Models for Discharge Prediction: Best Practices in Leveraging Electronic Health Record Audit Logs.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Electronic Health Record (EHR) audit log data are increasingly utilized for clinical tasks, from workflow modeling to predictive analyses of discharge events, adverse kidney outcomes, and hospital readmissions. These data encapsulate user-EHR interac...

Narrative Feature or Structured Feature? A Study of Large Language Models to Identify Cancer Patients at Risk of Heart Failure.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Cancer treatments are known to introduce cardiotoxicity, negatively impacting outcomes and survivorship. Identifying cancer patients at risk of heart failure (HF) is critical to improving cancer treatment outcomes and safety. This study examined mach...

A Large Language Model Outperforms Other Computational Approaches to the High-Throughput Phenotyping of Physician Notes.

AMIA ... Annual Symposium proceedings. AMIA Symposium
High-throughput phenotyping, the automated mapping of patient signs and symptoms to standardized ontology concepts, is essential for realizing value from electronic health records (EHR) in support of precision medicine. Despite technological advances...

Clinical Information Extraction with Large Language Models: A Case Study on Organ Procurement.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Recent work has demonstrated that large language models (LLMs) are powerful tools for clinical information extraction from unstructured text. However, existing approaches have largely ignored the extraction of numeric information such as laboratory t...

Using Large Language Models for sentiment analysis of health-related social media data: empirical evaluation and practical tips.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Health-related social media data generated by patients and the public provide valuable insights into patient experiences and opinions toward health issues such as vaccination and medical treatments. Using Natural Language Processing (NLP) methods to ...