A clarion call to introduce artificial intelligence (AI) in postgraduate medical physics curriculum

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.

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Focus issue: Artificial intelligence in medical physics

Physica Medica ◽

10.1016/j.ejmp.2021.05.008 ◽

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

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Medical Physics Contributes to The Advancement in Medicine

ASM Science Journal ◽

10.32802/asmscj.2020.505 ◽

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.

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The Emergence of Artificial Intelligence within Radiation Oncology Treatment Planning

Oncology ◽

10.1159/000512172 ◽

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

Background: 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? Summary: 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. Key Messages: 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.

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