Journal of imaging informatics in medicine
38558368
In recent years, the role of Artificial Intelligence (AI) in medical imaging has become increasingly prominent, with the majority of AI applications approved by the FDA being in imaging and radiology in 2023. The surge in AI model development to tack...
BACKGROUND: A large language model (LLM) is a machine learning model inferred from text data that captures subtle patterns of language use in context. Modern LLMs are based on neural network architectures that incorporate transformer methods. They al...
OBJECTIVE: Biomedical Named Entity Recognition (bio NER) is the task of recognizing named entities in biomedical texts. This paper introduces a new model that addresses bio NER by considering additional external contexts. Different from prior methods...
OBJECTIVE: Pneumothorax is an acute thoracic disease caused by abnormal air collection between the lungs and chest wall. Recently, artificial intelligence (AI), especially deep learning (DL), has been increasingly employed for automating the diagnost...
Journal of imaging informatics in medicine
38858261
Previously, the lack of a standard body part ontology has been identified as a critical deficiency needed to enable enterprise imaging. This whitepaper aims to provide a comprehensive assessment of anatomical ontologies with the aim of facilitating e...
OBJECTIVE: Understanding and quantifying biases when designing and implementing actionable approaches to increase fairness and inclusion is critical for artificial intelligence (AI) in biomedical applications.
Journal of the American Medical Informatics Association : JAMIA
38657567
OBJECTIVES: Generative large language models (LLMs) are a subset of transformers-based neural network architecture models. LLMs have successfully leveraged a combination of an increased number of parameters, improvements in computational efficiency, ...