AIMC Topic: Natural Language Processing
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Analyzing Patient Complaints in Web-Based Reviews of Private Hospitals in Selangor, Malaysia, Using Large Language Model-Assisted Content Analysis: Mixed Methods Study.
JMIR formative research
Jun 27, 2025
BACKGROUND: Large language model (LLM)-assisted content analysis (LACA) is a modification of traditional content analysis, leveraging the LLM to codevelop codebooks and automatically assign thematic codes to a web-based reviews dataset.
How well do multimodal LLMs interpret CT scans? An auto-evaluation framework for analyses.
Journal of biomedical informatics
Jun 25, 2025
OBJECTIVE: This study introduces a novel evaluation framework, GPTRadScore, to systematically assess the performance of multimodal large language models (MLLMs) in generating clinically accurate findings from CT imaging. Specifically, GPTRadScore lev...
Text intelligent correction in English translation: A study on integrating models with dependency attention mechanism.
PloS one
Jun 24, 2025
Improving translation quality and efficiency is one of the key challenges in the field of Natural Language Processing (NLP). This study proposes an enhanced model based on Bidirectional Encoder Representations from Transformers (BERT), combined with ...
A flexible two-stage anonymization framework for narrative medical records adapting to various language models.
Computers in biology and medicine
Jun 23, 2025
The healthcare sector increasingly relies on Electronic Health Records (EHRs) for efficient and high-quality patient care by providing rapid access to comprehensive medical information. However, these records contain sensitive patient data that must ...
Extracting critical clinical indicators and survival prediction of lung cancer from pathology reports using large language models.
Computers in biology and medicine
Jun 23, 2025
Lung cancer remains the leading cause of cancer deaths in many developed countries, primarily due to late-stage diagnosis. Histopathology, the gold standard for diagnosis, often results in semi-structured pathological reports containing complex infor...
SSMT-PANBERT: A single-stage multitask model for phenotype extraction and assertion negation detection in unstructured clinical text.
Computers in biology and medicine
Jun 22, 2025
Automatic phenotype extraction and assertion negation detection from large-scale accessible Electronic Health Records (EHRs), including discharge summaries and radiology reports, is a crucial task for various healthcare applications, such as disease ...
Zero- and few-shot Named Entity Recognition and Text Expansion in medication prescriptions using large language models.
Artificial intelligence in medicine
Jun 20, 2025
Medication prescriptions in electronic health records (EHR) are often in free-text and may include a mix of languages, local brand names, and a wide range of idiosyncratic formats and abbreviations. Large language models (LLMs) have shown a promising...
A comparative study of recent large language models on generating hospital discharge summaries for lung cancer patients.
Journal of biomedical informatics
Jun 20, 2025
OBJECTIVE: Generating discharge summaries is a crucial yet time-consuming task in clinical practice, essential for conveying pertinent patient information and facilitating continuity of care. Recent advancements in large language models (LLMs) have s...
Performance assessment of an artificial intelligence algorithm for opportunistic screening of abdominal aortic aneurysms.
Clinical imaging
Jun 19, 2025
PURPOSE: Abdominal aortic aneurysm (AAA) is a common incidental finding on CT imaging performed in the acute care setting. Artificial intelligence (AI) algorithms have been developed to automatically measure aortic lumen size and thus facilitate AAA ...
Assessing large language models for acute heart failure classification and information extraction from French clinical notes.
Computers in biology and medicine
Jun 19, 2025
Understanding acute heart failure (AHF) remains a significant challenge, as many clinical details are recorded in unstructured text rather than structured data in electronic health records (EHRs). In this study, we explored the use of large language ...
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