AIMC Topic: Large Language Models
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Rising Influence of Artificial Intelligence: Trends in Large Language Model Usage in Dermatology Residency Personal Statements.
Journal of drugs in dermatology : JDD
May 1, 2025
With the public release of ChatGPT before the 2024 dermatology residency application cycle, applicants gained access to an advanced language model capable of generating or enhancing personal statements. This study examines trends in artificial intell...
Exploratory Analysis of Nationwide Japanese Patient Safety Reports on Suicide and Suicide Attempts Among Inpatients With Cancer Using Large Language Models.
Psycho-oncology
May 1, 2025
OBJECTIVE: Patients with cancer have a high risk of suicide. However, evidence-based preventive measures remain unclear. This study aimed to investigate suicide prevention strategies for hospitalized patients with cancer by analyzing nationwide patie...
High-performance automated abstract screening with large language model ensembles.
Journal of the American Medical Informatics Association : JAMIA
May 1, 2025
OBJECTIVE: screening is a labor-intensive component of systematic review involving repetitive application of inclusion and exclusion criteria on a large volume of studies. We aimed to validate large language models (LLMs) used to automate abstract sc...
Robust privacy amidst innovation with large language models through a critical assessment of the risks.
Journal of the American Medical Informatics Association : JAMIA
May 1, 2025
OBJECTIVE: This study evaluates the integration of electronic health records (EHRs) and natural language processing (NLP) with large language models (LLMs) to enhance healthcare data management and patient care, focusing on using advanced language mo...
Utilizing large language models for detecting hospital-acquired conditions: an empirical study on pulmonary embolism.
Journal of the American Medical Informatics Association : JAMIA
May 1, 2025
OBJECTIVES: Adverse event detection from Electronic Medical Records (EMRs) is challenging due to the low incidence of the event, variability in clinical documentation, and the complexity of data formats. Pulmonary embolism as an adverse event (PEAE) ...
Enhancing Large Language Models with Retrieval-Augmented Generation: A Radiology-Specific Approach.
Radiology. Artificial intelligence
May 1, 2025
Retrieval-augmented generation (RAG) is a strategy to improve the performance of large language models (LLMs) by providing an LLM with an updated corpus of knowledge that can be used for answer generation in real time. RAG may improve LLM performance...
LLMER: Crafting Interactive Extended Reality Worlds with JSON Data Generated by Large Language Models.
IEEE transactions on visualization and computer graphics
May 1, 2025
The integration of Large Language Models (LLMs) like GPT-4 with Extended Reality (XR) technologies offers the potential to build truly immersive XR environments that interact with human users through natural language, e.g., generating and animating 3...
Large language models are less effective at clinical prediction tasks than locally trained machine learning models.
Journal of the American Medical Informatics Association : JAMIA
May 1, 2025
OBJECTIVES: To determine the extent to which current large language models (LLMs) can serve as substitutes for traditional machine learning (ML) as clinical predictors using data from electronic health records (EHRs), we investigated various factors ...
Evaluating the Performance of Large Language Models (LLMs) in Answering and Analysing the Chinese Dental Licensing Examination.
European journal of dental education : official journal of the Association for Dental Education in Europe
May 1, 2025
BACKGROUND: This study aimed to simulate diverse scenarios of students employing LLMs for CDLE examination preparation, providing a detailed evaluation of their performance in medical education.
Enhancing Oncological Surveillance Through Large Language Model-Assisted Analysis: A Comparative Study of GPT-4 and Gemini in Evaluating Oncological Issues From Serial Abdominal CT Scan Reports.
Academic radiology
May 1, 2025
RATIONALE AND OBJECTIVES: We aimed to compare the capabilities of two leading large language models (LLMs), GPT-4 and Gemini, in analyzing serial radiology reports, to highlight oncological issues that require further clinical attention.
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