The 2014 initiative is not only unnecessary but it constitutes a threat to the future of medical physics

2011 ◽  
Vol 38 (10) ◽  
pp. 5267-5269
Author(s):  
Gary D. Fullerton ◽  
Kenneth N. Vanek ◽  
Colin G. Orton
Keyword(s):  
Medical Physics ◽  
The Future

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.

MO-C-BRA-03: The Future of Medical Physics: Challenges and Opportunities

2011 ◽  
Vol 38 (6Part25) ◽  
pp. 3708-3708
Author(s):  
J Boone ◽  
R Fahrig ◽  
D Rogers ◽  
G Starkschall ◽  
P Halvorsen ◽  
...  
Keyword(s):  
Medical Physics ◽  
The Future ◽  
Challenges And Opportunities

Education and training of medical physics in Iran: The past, the present and the future

2017 ◽  
Vol 36 ◽  
pp. 66-72 ◽  
Author(s):  
Seyed Rabi Mahdavi ◽  
Behrouz Rasuli ◽  
Azam Niroomand-Rad
Keyword(s):  
Medical Physics ◽  
Education And Training ◽  
The Past ◽  
The Future ◽  
And Training

scholarly journals SP-0646: Medical physics for the future: How to get ready?

2013 ◽  
Vol 106 ◽  
pp. S248
Author(s):  
R. Jeraj ◽  
T. Bortfeld
Keyword(s):  
Medical Physics ◽  
The Future

The Development of Medical Physics in Vietnam: The Past, the Present and the Future

2017 ◽  
pp. 341-344
Author(s):  
Kieu-Trang T. Hoang ◽  
Lawrence H. Le ◽  
Trung-Hieu Nguyen ◽  
Tan-Chau Nguyen ◽  
Hong-Loan T. Truong ◽  
...  
Keyword(s):  
Medical Physics ◽  
The Past ◽  
The Future
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