AIMC Topic: Large Language Models
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Physician awareness of, interest in, and current use of artificial intelligence large language model-based virtual assistants.
PloS one
Jan 1, 2025
There is increasing medical interest and research regarding the potential of large language model-based virtual assistants in healthcare. It is important to understand physicians' interest in implementing these tools into clinical practice, so preced...
Careful design of Large Language Model pipelines enables expert-level retrieval of evidence-based information from syntheses and databases.
PloS one
Jan 1, 2025
Wise use of evidence to support efficient conservation action is key to tackling biodiversity loss with limited time and resources. Evidence syntheses provide key recommendations for conservation decision-makers by assessing and summarising evidence,...
A comparative analysis of large language models versus traditional information extraction methods for real-world evidence of patient symptomatology in acute and post-acute sequelae of SARS-CoV-2.
PloS one
Jan 1, 2025
BACKGROUND: Patient symptoms, crucial for disease progression and diagnosis, are often captured in unstructured clinical notes. Large language models (LLMs) offer potential advantages in extracting patient symptoms compared to traditional rule-based ...
Comparitive performance of artificial intelligence-based large language models on the orthopedic in-training examination.
Journal of orthopaedic surgery (Hong Kong)
Jan 1, 2025
BACKGROUND: Large language models (LLMs) have many clinical applications. However, the comparative performance of different LLMs on orthopedic board style questions remains largely unknown.
Enhancing Patient Education on Cardiovascular Rehabilitation with Large Language Models.
Missouri medicine
Jan 1, 2025
INTRODUCTION: There are barriers that exist for individuals to adhere to cardiovascular rehabilitation programs. A key driver to patient adherence is appropriately educating patients. A growing education tool is using large language models to answer ...
Large language models as an academic resource for radiologists stepping into artificial intelligence research.
Current problems in diagnostic radiology
Jan 1, 2025
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
Jul 1, 2024
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
Jan 1, 2024
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
Jan 1, 2024
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
Jan 1, 2024
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...
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