Artificial intelligence will reduce the need for clinical medical physicists

Tang, Xiaoli; Wang, Brian; Rong, Yi

doi:10.1002/acm2.12244

Cited by 33 publications

(23 citation statements)

References 13 publications

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“…According to a compilation of surveys by Kisling et al [69] in 2018, medical physicists on average spent 87% of their time performing “clinical service and consultation,” 7% of their time performing “research and development,” and 5% of their time “teaching.” Given that most respondents (>90%) perform plan review and weekly chart checks [69], physicists may spend a larger portion their time in the future on non-automatable tasks such as quality improvement, FMEA, research, or teaching (given that clinical workload remains constant). As suggested by Tang [9], the physicist may find that he or she has more time to perform tasks that are considered to be “non-routine” activities (e.g., commissioning, implementing new technology, and patient-specific consultations). Also important for the future workforce, as noted by Atwood et al [75], may be a more direct role in patient care responsibilities, such as a physicist-patient or technical consult.…”

Section: Impact Of Emerging Technologymentioning

confidence: 99%

The Emergence of Artificial Intelligence within Radiation Oncology Treatment Planning

et al. 2020

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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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Section: Impact Of Emerging Technologymentioning

confidence: 99%

The Emergence of Artificial Intelligence within Radiation Oncology Treatment Planning

et al. 2020

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“…In RT, much of the effort expended on time-consuming manual activities, for example, chart checks; IMRT and LINAC QA; and plan checks, appears to be of questionable clinical value given the high reliability and integration of modern imaging, planning, and delivery platforms, or is at risk from automation and artificial intelligence. [7][8][9] Effort expended on inverse-and forward-planning may be substantially reduced by robust knowledge-based planning schemes. On the other hand, new and emerging technologies, for example, adaptive RT, MRI-guided RT, 4D RT, and wider use of extreme hypofractionation, are good examples of how the fruits of medical physics research provide new opportunities 10 for clinical physicists to add value to the clinical enterprise.…”

Section: Opening Statementmentioning

confidence: 99%

The current CAMPEP graduate program didactic course guidelines have insufficiently rigorous requirements for research training

Williamson

Das

White³

2020

Medical Physics

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“…Although AI may replace several routine tasks related to medical physicist work in the future, as discussed in recent point/counterpoint publications [39,40], it will also generate new ones [41]. There is a transition in our role towards more comprehensive expertise and clinically relevant impact from our knowledge [42,43].…”

Section: How Can Medical Physicists Start To Prepare?mentioning

confidence: 99%

The European Federation of Organisations for Medical Physics (EFOMP) White Paper: Big data and deep learning in medical imaging and in relation to medical physics profession

et al. 2018

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Big data and deep learning will profoundly change various areas of professions and research in the future. This will also happen in medicine and medical imaging in particular. As medical physicists, we should pursue beyond the concept of technical quality to extend our methodology and competence towards measuring and optimising the diagnostic value in terms of how it is connected to care outcome. Functional implementation of such methodology requires data processing utilities starting from data collection and management and culminating in the data analysis methods. Data quality control and validation are prerequisites for the deep learning application in order to provide reliable further analysis, classification, interpretation, probabilistic and predictive modelling from the vast heterogeneous big data. Challenges in practical data analytics relate to both horizontal and longitudinal analysis aspects. Quantitative aspects of data validation, quality control, physically meaningful measures, parameter connections and system modelling for the future artificial intelligence (AI) methods are positioned firmly in the field of Medical Physics profession. It is our interest to ensure that our professional education, continuous training and competence will follow this significant global development.

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Artificial intelligence will reduce the need for clinical medical physicists

Cited by 33 publications

References 13 publications

The Emergence of Artificial Intelligence within Radiation Oncology Treatment Planning

The Emergence of Artificial Intelligence within Radiation Oncology Treatment Planning

The current CAMPEP graduate program didactic course guidelines have insufficiently rigorous requirements for research training

The European Federation of Organisations for Medical Physics (EFOMP) White Paper: Big data and deep learning in medical imaging and in relation to medical physics profession

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