[HTML][HTML] Large language models and the emergence phenomena

V Sorin, E Klang - European Journal of Radiology Open, 2023 - Elsevier
This perspective explores the potential of emergence phenomena in large language models
(LLMs) to transform data management and analysis in radiology. We provide a concise …

ESR Journals editors' joint statement on Guidelines for the Use of Large Language Models by Authors, Reviewers, and Editors

B Hamm, L Marti-Bonmati, F Sardanelli - Insights into Imaging, 2024 - Springer
The impact of artificial intelligence (AI)-assisted technologies, such as Large Language
Models (LLMs), chatbots, or image creators, on biomedical publishing was discussed by the …

The long but necessary road to responsible use of large language models in healthcare research

JCC Kwong, SCY Wang, GC Nickel… - npj Digital …, 2024 - nature.com
Large language models (LLMs) have shown promise in reducing time, costs, and errors
associated with manual data extraction. A recent study demonstrated that LLMs …

Natural language processing in radiology: Clinical applications and future directions

PS Bobba, A Sailer, JA Pruneski, S Beck, A Mozayan… - Clinical Imaging, 2023 - Elsevier
Natural language processing (NLP) is a wide range of techniques that allows computers to
interact with human text. Applications of NLP in everyday life include language translation …

Evaluating large language models for radiology natural language processing

Z Liu, T Zhong, Y Li, Y Zhang, Y Pan, Z Zhao… - arXiv preprint arXiv …, 2023 - arxiv.org
The rise of large language models (LLMs) has marked a pivotal shift in the field of natural
language processing (NLP). LLMs have revolutionized a multitude of domains, and they …

[HTML][HTML] Focus: Big Data: Artificial Intelligence to Improve Patient Understanding of Radiology Reports

K Amin, P Khosla, R Doshi, S Chheang… - The Yale Journal of …, 2023 - ncbi.nlm.nih.gov
Diagnostic imaging reports are generally written with a target audience of other providers.
As a result, the reports are written with medical jargon and technical detail to ensure …

Guidelines for use of large language models by authors, reviewers, and editors: considerations for imaging journals

L Moy - Radiology, 2023 - pubs.rsna.org
5. Authors should be able to assert that there is no plagiarism in their paper, including in text
and images produced by AI. Humans must ensure appropriate attribution to all quoted …

A comprehensive survey of large language models and multimodal large language models in medicine

H Xiao, F Zhou, X Liu, T Liu, Z Li, X Liu… - arXiv preprint arXiv …, 2024 - arxiv.org
Since the release of ChatGPT and GPT-4, large language models (LLMs) and multimodal
large language models (MLLMs) have garnered significant attention due to their powerful …

Large language models illuminate a progressive pathway to artificial healthcare assistant: A review

M Yuan, P Bao, J Yuan, Y Shen, Z Chen, Y Xie… - arXiv preprint arXiv …, 2023 - arxiv.org
With the rapid development of artificial intelligence, large language models (LLMs) have
shown promising capabilities in mimicking human-level language comprehension and …

[HTML][HTML] Large language models: a guide for radiologists

S Kim, C Lee, S Kim - Korean Journal of Radiology, 2024 - ncbi.nlm.nih.gov
Large language models (LLMs) have revolutionized the global landscape of technology
beyond natural language processing. Owing to their extensive pre-training on vast datasets …