Research Highlight: Use of Generative Images Created with Artificial Intelligence for Brain Tumor Imaging

Park, Ji Eun; Vollmuth, Philipp; Kim, Namkug; Kim, Ho Sung

doi:10.3348/kjr.2022.0033

Cited by 7 publications

(5 citation statements)

References 19 publications

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“…GANs are versatile and effective tools for advancing medical imaging and analysis. Moreover, they have effectively improved the deep learning performance for various radiology tasks, including lesion detection, organ segmentation, and the prediction of patient outcomes, via data augmentation [ 84 85 86 87 88 89 ]. GANs have also been used in image registration to yield more accurate results.…”

Section: Potential and Application Of Generative Models In Clinical I...mentioning

confidence: 99%

Updated Primer on Generative Artificial Intelligence and Large Language Models in Medical Imaging for Medical Professionals

Kim,

Cho,

Jang

et al. 2024

Korean J Radiol

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The emergence of Chat Generative Pre-trained Transformer (ChatGPT), a chatbot developed by OpenAI, has garnered interest in the application of generative artificial intelligence (AI) models in the medical field. This review summarizes different generative AI models and their potential applications in the field of medicine and explores the evolving landscape of Generative Adversarial Networks and diffusion models since the introduction of generative AI models. These models have made valuable contributions to the field of radiology. Furthermore, this review also explores the significance of synthetic data in addressing privacy concerns and augmenting data diversity and quality within the medical domain, in addition to emphasizing the role of inversion in the investigation of generative models and outlining an approach to replicate this process. We provide an overview of Large Language Models, such as GPTs and bidirectional encoder representations (BERTs), that focus on prominent representatives and discuss recent initiatives involving language-vision models in radiology, including innovative large language and vision assistant for biomedicine (LLaVa-Med), to illustrate their practical application. This comprehensive review offers insights into the wide-ranging applications of generative AI models in clinical research and emphasizes their transformative potential.

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Section: Potential and Application Of Generative Models In Clinical I...mentioning

confidence: 99%

Updated Primer on Generative Artificial Intelligence and Large Language Models in Medical Imaging for Medical Professionals

Kim,

Cho,

Jang

et al. 2024

Korean J Radiol

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“…The images obtained ultimately comprise data inputs and promote the effectiveness of DL techniques. Because unusual tumour types and the use of multiparametric imaging sequences sometimes lead to limited or partial imaging datasets, brain tumour imaging utilizing MRI is of special relevance for the clinical implementation of image production using GAN (Park et al, 2022). GANs are also widely used to generate super‐resolution images in medical imaging.…”

Section: Applications Of Deep and Machine Learning In Medical Fieldsmentioning

confidence: 99%

“…The likelihood of various gene interactions is then calculated using Bayesian inference based on the observed data. For example, in the case of identifying the genetic pathways underlying the disease, gene expression data from brain tissue samples of patients with Alzheimer's disease and healthy controls can be used to construct a Bayesian network that models the interactions between genes and other pertinent biological variables like age, gender, and brain region (Gupta et al, 2020; Park et al, 2022). The likelihood of various gene connections is calculated using Bayesian inference once the network has been constructed based on what has been observed.…”

Section: Applications Of Deep and Machine Learning In Medical Fieldsmentioning

confidence: 99%

Advancements in medical diagnosis and treatment through machine learning: A review

Ahsan,

Khan,

Khan

et al. 2023

Expert Systems

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The aptness of machine learning (ML) to learn from large datasets, discover trends, and make predictions has demonstrated its potential to metamorphose the medical field. Medical data analysis with ML algorithms can improve patient outcomes in terms of both treatment and diagnosis. This paper investigates the numerous possibilities of ML in the medical industries, including radiology, pathology, genomics, and clinical decision‐making. It also goes over the benefits and drawbacks of ML in various sectors as well as the limitations that come with its application. It illustrates the potential advantages of ML, such as better accuracy and efficiency in diagnosis and individualized treatment programs, through a review of previous studies. Lastly, it provides perspectives on prospective advancements and prospects for the discipline. This study also intends to investigate the applications of deep learning (DL) in the medical field. DL algorithms have performed exceptionally satisfactorily in several healthcare‐related fields. The main conclusions of the study are summarized, and their ramifications for the healthcare sector are discussed in this paper's conclusion. This paper intends to contribute to a greater understanding of the prevailing state of the discipline and the possibility for future developments by emphasizing the prospects of these methodologies to alter medical study and clinical practice.

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“…Another notable change in KJR in 2022 was the increased number of brief articles published. These include focused or mini-reviews, expert opinion or perspective types of articles, and commentaries [ 16 17 18 22 23 24 25 26 27 28 29 30 31 32 33 ]. Publication of these brief articles is in consideration of a recent trend in scientific publication.…”

mentioning

confidence: 99%

“…These editorials may be a new and quick way for most readers to keep up with a particular issue/field. KJR occasionally published commentary-type articles to help readers identify and digest critical information created outside KJR or even outside the radiology field [ 23 24 26 28 31 33 ], instead of studies published in KJR [ 27 35 ]. Starting early 2023, KJR plans to publish more commentary-style articles for noteworthy studies published in KJR .…”

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confidence: 99%

Looking Back at 2022 and ahead to 2023 for the Korean Journal of Radiology

Park

2023

Korean J Radiol

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Research Highlight: Use of Generative Images Created with Artificial Intelligence for Brain Tumor Imaging

Cited by 7 publications

References 19 publications

Updated Primer on Generative Artificial Intelligence and Large Language Models in Medical Imaging for Medical Professionals

Updated Primer on Generative Artificial Intelligence and Large Language Models in Medical Imaging for Medical Professionals

Advancements in medical diagnosis and treatment through machine learning: A review

Looking Back at 2022 and ahead to 2023 for the Korean Journal of Radiology

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