Artificial intelligence will soon change the landscape of medical physics research and practice

2018 ◽  
Vol 45 (5) ◽  
pp. 1791-1793 ◽  
Author(s):  
Lei Xing ◽  
Elizabeth A. Krupinski ◽  
Jing Cai
Keyword(s):  
Artificial Intelligence ◽  
Medical Physics ◽  
Research And Practice

Top 100 most-cited articles on medical imaging applications of artificial intelligence: a bibliometric analysis (Preprint)

2020 ◽  
Author(s):  
Ying Liu ◽  
Ziyan Yu ◽  
Shuolan Jing ◽  
Honghu Jiang ◽  
Chunxia Wang
Keyword(s):  
Artificial Intelligence ◽  
Medical Imaging ◽  
Bibliometric Analysis ◽  
Clinical Medicine ◽  
Medical Physics ◽  
Medical Image Analysis ◽  
The United States ◽  
Independent Learning ◽  
Learning Ability ◽  
Core Technology

BACKGROUND Artificial intelligence (AI) has penetrated into almost every aspect of our lives and is rapidly changing our way of life. Recently, the new generation of AI taking machine learning and particularly deep convolutional neural network theories as the core technology, has stronger learning ability and independent learning evolution ability, combined with a large amount of learning data, breaks through the bottleneck limit of model accuracy, and makes the model efficient use. OBJECTIVE To identify the 100 most cited papers in artificial intelligence in medical imaging, we performed a comprehensive bibliometric analysis basing on the literature search on Web of Science Core Collection (WoSCC). METHODS The 100 top-cited articles published in “AI, Medical imaging” journals were identified using the Science Citation Index Database. The articles were further reviewed, and basic information was collected, including the number of citations, journals, authors, publication year, and field of study. RESULTS The highly cited articles in AI were cited between 72 and 1,554 times. The majority of them were published in three major journals: IEEE Transactions on Medical Imaging, Medical Image Analysis and Medical Physics. The publication year ranged from 2002 to 2019, with 66% published in a three-year period (2016 to 2018). Publications from the United States (56%) were the most heavily cited, followed by those from China (15%) and Netherlands (10%). Radboud University Nijmegen from Netherlands, Harvard Medical School in USA, and The Chinese University of Hong Kong in China produced the highest number of publications (n=6). Computer science (42%), clinical medicine (35%), and engineering (8%) were the most common fields of study. CONCLUSIONS Citation analysis in the field of artificial intelligence in medical imaging reveals interesting information about the topics and trends negotiated by researchers and elucidates which characteristics are required for a paper to attain a “classic” status. Clinical science articles published in highimpact specialized journals are most likely to be cited in the field of artificial intelligence in medical imaging.

scholarly journals Focus issue: Artificial intelligence in medical physics

2021 ◽  
Vol 83 ◽  
pp. 287-291
Author(s):  
F. Zanca ◽  
I.M. Avanzo ◽  
N. Colgan ◽  
W. Crijns ◽  
G. Guidi ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Medical Physics

scholarly journals Artificial Intelligence in Education (AIEd): a high-level academic and industry note 2021

AI and Ethics ◽  
2021 ◽  
Author(s):  
Muhammad Ali Chaudhry ◽  
Emre Kazim
Keyword(s):  
Artificial Intelligence ◽  
Intelligent Tutoring Systems ◽  
Policy Makers ◽  
Tutoring Systems ◽  
Research And Practice ◽  
The Past ◽  
Artificial Intelligence In Education ◽  
Potential Impact ◽  
High Level ◽  
Contextualized Learning

AbstractIn the past few decades, technology has completely transformed the world around us. Indeed, experts believe that the next big digital transformation in how we live, communicate, work, trade and learn will be driven by Artificial Intelligence (AI) [83]. This paper presents a high-level industrial and academic overview of AI in Education (AIEd). It presents the focus of latest research in AIEd on reducing teachers’ workload, contextualized learning for students, revolutionizing assessments and developments in intelligent tutoring systems. It also discusses the ethical dimension of AIEd and the potential impact of the Covid-19 pandemic on the future of AIEd’s research and practice. The intended readership of this article is policy makers and institutional leaders who are looking for an introductory state of play in AIEd.

scholarly journals Medical Physics Contributes to The Advancement in Medicine

2021 ◽  
Vol 14 ◽  
pp. 1-7
Author(s):  
Kwan Hoong Ng ◽  
Jeannie Hsiu Ding Wong ◽  
Chai Hong Yeong ◽  
Hafiz Mohd Zin ◽  
Noriah Jamal
Keyword(s):  
Artificial Intelligence ◽  
Nuclear Medicine ◽  
Big Data ◽  
New Technologies ◽  
Medical Physics ◽  
Knowledge And Skills ◽  
X Ray ◽  
Medical Physicists ◽  
Medicine Today ◽  
Improve Health

Medical physics is the application of physics principles and techniques in medicine. Medical physicists are actively applying their knowledge and skills in the prevention, diagnosis and treatment of diseases to improve health via research and clinical practice. In this paper, we present the roles of medical physicists in the three primary fields, namely, diagnostic imaging, radiotherapy and nuclear medicine.  Medical physicists have been playing a crucial role in the advancement of new technologies that have revolutionised medicine today. This includes the continuous development of medical imaging and radiotherapy techniques since the discovery of X-ray and radioactivity. The last decade has seen tremendous development in the field that allows for better diagnosis and targeted treatment of various diseases. In the era of big data and artificial intelligence, while medical physicists continue to ensure that the application of the technologies in medicine is optimal and safe, it is paramount for the profession to evolve and be equipped with new skills to continue to contribute to the advancement of medicine.

scholarly journals Machine Learning in Oncology: What Should Clinicians Know?

2020 ◽  
pp. 799-810
Author(s):  
Matthew Nagy ◽  
Nathan Radakovich ◽  
Aziz Nazha
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Clinical Data ◽  
Cancer Diagnosis ◽  
Health Data ◽  
Computer Processing ◽  
Research And Practice ◽  
New Methods ◽  
Oncology Research ◽  
Processing Power

The volume and complexity of scientific and clinical data in oncology have grown markedly over recent years, including but not limited to the realms of electronic health data, radiographic and histologic data, and genomics. This growth holds promise for a deeper understanding of malignancy and, accordingly, more personalized and effective oncologic care. Such goals require, however, the development of new methods to fully make use of the wealth of available data. Improvements in computer processing power and algorithm development have positioned machine learning, a branch of artificial intelligence, to play a prominent role in oncology research and practice. This review provides an overview of the basics of machine learning and highlights current progress and challenges in applying this technology to cancer diagnosis, prognosis, and treatment recommendations, including a discussion of current takeaways for clinicians.

scholarly journals Artificial Intelligence Imagery Analysis Fostering Big Data Analytics

2019 ◽  
Vol 11 (8) ◽  
pp. 178 ◽  
Author(s):  
Stefan Cremer ◽  
Claudia Loebbecke
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Quality Data ◽  
Background Information ◽  
Research And Practice ◽  
Management Approaches ◽  
Thumbnail Images

In an era of accelerating digitization and advanced big data analytics, harnessing quality data and insights will enable innovative research methods and management approaches. Among others, Artificial Intelligence Imagery Analysis has recently emerged as a new method for analyzing the content of large amounts of pictorial data. In this paper, we provide background information and outline the application of Artificial Intelligence Imagery Analysis for analyzing the content of large amounts of pictorial data. We suggest that Artificial Intelligence Imagery Analysis constitutes a profound improvement over previous methods that have mostly relied on manual work by humans. In this paper, we discuss the applications of Artificial Intelligence Imagery Analysis for research and practice and provide an example of its use for research. In the case study, we employed Artificial Intelligence Imagery Analysis for decomposing and assessing thumbnail images in the context of marketing and media research and show how properly assessed and designed thumbnail images promote the consumption of online videos. We conclude the paper with a discussion on the potential of Artificial Intelligence Imagery Analysis for research and practice across disciplines.

scholarly journals The Emergence of Artificial Intelligence within Radiation Oncology Treatment Planning

Oncology ◽  
2020 ◽  
pp. 1-11
Author(s):  
Tucker J. Netherton ◽  
Carlos E. Cardenas ◽  
Dong Joo Rhee ◽  
Laurence E. Court ◽  
Beth M. Beadle
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Treatment Planning ◽  
Radiation Oncology ◽  
Radiation Treatment ◽  
Medical Physics ◽  
External Beam Radiotherapy ◽  
Radiation Treatment Planning ◽  
Planning Team ◽  
The Future

<b><i>Background:</i></b> The future of artificial intelligence (AI) heralds unprecedented change for the field of radiation oncology. Commercial vendors and academic institutions have created AI tools for radiation oncology, but such tools have not yet been widely adopted into clinical practice. In addition, numerous discussions have prompted careful thoughts about AI’s impact upon the future landscape of radiation oncology: How can we preserve innovation, creativity, and patient safety? When will AI-based tools be widely adopted into the clinic? Will the need for clinical staff be reduced? How will these devices and tools be developed and regulated? <b><i>Summary:</i></b> In this work, we examine how deep learning, a rapidly emerging subset of AI, fits into the broader historical context of advancements made in radiation oncology and medical physics. In addition, we examine a representative set of deep learning-based tools that are being made available for use in external beam radiotherapy treatment planning and how these deep learning-based tools and other AI-based tools will impact members of the radiation treatment planning team. <b><i>Key Messages:</i></b> Compared to past transformative innovations explored in this article, such as the Monte Carlo method or intensity-modulated radiotherapy, the development and adoption of deep learning-based tools is occurring at faster rates and promises to transform practices of the radiation treatment planning team. However, accessibility to these tools will be determined by each clinic’s access to the internet, web-based solutions, or high-performance computing hardware. As seen by the trends exhibited by many technologies, high dependence on new technology can result in harm should the product fail in an unexpected manner, be misused by the operator, or if the mitigation to an expected failure is not adequate. Thus, the need for developers and researchers to rigorously validate deep learning-based tools, for users to understand how to operate tools appropriately, and for professional bodies to develop guidelines for their use and maintenance is essential. Given that members of the radiation treatment planning team perform many tasks that are automatable, the use of deep learning-based tools, in combination with other automated treatment planning tools, may refocus tasks performed by the treatment planning team and may potentially reduce resource-related burdens for clinics with limited resources.

Artificial Intelligence: A Strategic Disruption in Public Relations

2019 ◽  
Vol 14 (3) ◽  
pp. 196-213 ◽  
Author(s):  
Geetanjali Panda ◽  
Ashwani Kumar Upadhyay ◽  
Komal Khandelwal
Keyword(s):  
Artificial Intelligence ◽  
Social Media ◽  
Public Relations ◽  
Structured Interviews ◽  
Relevant Research ◽  
Research Papers ◽  
Research And Practice ◽  
Mass Personalization

This article discusses the concept, benefits, application, impact and role of artificial intelligence (AI) in public relations (PR) industry. It examines the application of AI-based systems and their role as strategic disruption in the PR industry. This article is based on qualitative semi-structured interviews of 31 PR professionals and is grounded in the insights from the review of relevant research papers, articles, and case studies. It highlights the developments in research and practice related to AI application in the PR industry. AI-powered systems can scan social media and are smart, intelligent and experts in handling queries. These AI-enabled systems can post responses on social media in real time for the client and manage the crisis. With AI, PR professionals can save time spent on mundane activities like creating media lists, scheduling meetings and sending follow-up emails. Mass personalization and customization using AI are improving the effectiveness of PR activities. It is too early to say whether AI will act as strategic disruption in the PR industry. Based on the insights and discussion in this article, the PR professionals and researchers can make decisions on whether to invest in AI tools and solutions.

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