AI-assisted clinical decision making (CDM) for dose prescription in radiosurgery of brain metastases using three-path three-dimensional CNN

Cao, Yu; Kunaprayoon, Dan; Xu, Junliang; Ren, Lei

doi:10.1016/j.ctro.2022.100565

Cited by 5 publications

(3 citation statements)

References 26 publications

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“…The system can use computer technology to monitor and evaluate personal health in real time to identify abnormal situations and take timely intervention measures (von Gerich et al, 2022;Koski and Murphy, 2021). According to the different physical conditions of individuals, targeted health prescriptions are formulated to assist in accurate medical care (Johnson et al, 2021;Cao et al,…”

Section: Tcm Health Managementmentioning

confidence: 99%

Integrating artificial intelligence into the modernization of traditional Chinese medicine industry: a review

Zhou,

Shen,

Hou

2024

Front. Pharmacol.

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Traditional Chinese medicine (TCM) is the practical experience and summary of the Chinese nation for thousands of years. It shows great potential in treating various chronic diseases, complex diseases and major infectious diseases, and has gradually attracted the attention of people all over the world. However, due to the complexity of prescription and action mechanism of TCM, the development of TCM industry is still in a relatively conservative stage. With the rise of artificial intelligence technology in various fields, many scholars began to apply artificial intelligence technology to traditional Chinese medicine industry and made remarkable progress. This paper comprehensively summarizes the important role of artificial intelligence in the development of traditional Chinese medicine industry from various aspects, including new drug discovery, data mining, quality standardization and industry technology of traditional Chinese medicine. The limitations of artificial intelligence in these applications are also emphasized, including the lack of pharmacological research, database quality problems and the challenges brought by human-computer interaction. Nevertheless, the development of artificial intelligence has brought new opportunities and innovations to the modernization of traditional Chinese medicine. Integrating artificial intelligence technology into the comprehensive application of Chinese medicine industry is expected to overcome the major problems faced by traditional Chinese medicine industry and further promote the modernization of the whole traditional Chinese medicine industry.

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Section: Tcm Health Managementmentioning

confidence: 99%

Integrating artificial intelligence into the modernization of traditional Chinese medicine industry: a review

Zhou,

Shen,

Hou

2024

Front. Pharmacol.

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“…This model not only addresses the need for advanced healthcare solutions but also emphasises user-friendliness, particularly benefiting patients in less developed regions. Similarly, Cao et al 18 have developed a convolutional neural network (CNN) model tailored for predicting dose prescriptions in complex clinical scenarios like radiation therapy. Additionally, large language models (LLM) such as ChatGPT 19 are now extensively employed in clinical and medical research, reshaping healthcare education, aiding in drug discovery, and refining personalised medicine.…”

Section: Introductionmentioning

confidence: 99%

Revolutionising healthcare with artificial intelligence: A bibliometric analysis of 40 years of progress in health systems

Hussain,

Mabrok,

Gao

et al. 2024

DIGITAL HEALTH

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The development of artificial intelligence (AI) has revolutionised the medical system, empowering healthcare professionals to analyse complex nonlinear big data and identify hidden patterns, facilitating well-informed decisions. Over the last decade, there has been a notable trend of research in AI, machine learning (ML), and their associated algorithms in health and medical systems. These approaches have transformed the healthcare system, enhancing efficiency, accuracy, personalised treatment, and decision-making. Recognising the importance and growing trend of research in the topic area, this paper presents a bibliometric analysis of AI in health and medical systems. The paper utilises the Web of Science (WoS) Core Collection database, considering documents published in the topic area for the last four decades. A total of 64,063 papers were identified from 1983 to 2022. The paper evaluates the bibliometric data from various perspectives, such as annual papers published, annual citations, highly cited papers, and most productive institutions, and countries. The paper visualises the relationship among various scientific actors by presenting bibliographic coupling and co-occurrences of the author's keywords. The analysis indicates that the field began its significant growth in the late 1970s and early 1980s, with significant growth since 2019. The most influential institutions are in the USA and China. The study also reveals that the scientific community's top keywords include ‘ML’, ‘Deep Learning’, and ‘Artificial Intelligence’.

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“…Brain image segmentation is a subject addressed by a vast number of researchers who seek to develop systems for accurate cancer diagnosis able to differentiate cancer cells from healthy ones [105][106][107][108][109][110][111]. A problem that such approaches can mitigate is that human verification of magnetic resonance imaging to locate tumors can be prone to errors.…”

mentioning

confidence: 99%

Developments in Image Processing Using Deep Learning and Reinforcement Learning

Valente,

António,

Mora

et al. 2023

J. Imaging

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The growth in the volume of data generated, consumed, and stored, which is estimated to exceed 180 zettabytes in 2025, represents a major challenge both for organizations and for society in general. In addition to being larger, datasets are increasingly complex, bringing new theoretical and computational challenges. Alongside this evolution, data science tools have exploded in popularity over the past two decades due to their myriad of applications when dealing with complex data, their high accuracy, flexible customization, and excellent adaptability. When it comes to images, data analysis presents additional challenges because as the quality of an image increases, which is desirable, so does the volume of data to be processed. Although classic machine learning (ML) techniques are still widely used in different research fields and industries, there has been great interest from the scientific community in the development of new artificial intelligence (AI) techniques. The resurgence of neural networks has boosted remarkable advances in areas such as the understanding and processing of images. In this study, we conducted a comprehensive survey regarding advances in AI design and the optimization solutions proposed to deal with image processing challenges. Despite the good results that have been achieved, there are still many challenges to face in this field of study. In this work, we discuss the main and more recent improvements, applications, and developments when targeting image processing applications, and we propose future research directions in this field of constant and fast evolution.

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AI-assisted clinical decision making (CDM) for dose prescription in radiosurgery of brain metastases using three-path three-dimensional CNN

Cited by 5 publications

References 26 publications

Integrating artificial intelligence into the modernization of traditional Chinese medicine industry: a review

Integrating artificial intelligence into the modernization of traditional Chinese medicine industry: a review

Revolutionising healthcare with artificial intelligence: A bibliometric analysis of 40 years of progress in health systems

Developments in Image Processing Using Deep Learning and Reinforcement Learning

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