The Emergence of Artificial Intelligence within Radiation Oncology Treatment Planning.
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, num...
| Publicado en: | Oncology Vol. 99; no. 2; pp. 124 - 135 |
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| Autores principales: | , , , , |
| Formato: | tables/charts Journal Article |
| Publicado: |
Karger AG
2021
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=148651140&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 148651140 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 00302414 NK2 jtl: Oncology issn: 00302414 maglogo: N pubinfo: dt: 2021 vid: 99 iid: 2 pid: 2485 pub: Karger AG artinfo: ui: 148651140 147723698 148651140 148651140 10.1159/000512172 148651140 ppf: 124 ppct: 11 formats: tig: atl: The Emergence of Artificial Intelligence within Radiation Oncology Treatment Planning. aug: au: Netherton, Tucker J. Cardenas, Carlos E. Rhee, Dong Joo Court, Laurence E. Beadle, Beth M. affil: Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA sug: subj: Artificial Intelligence Radiation Oncology Neoplasms Radiotherapy Deep Learning Patient Care Plans Tumor Burden Diffusion of Innovation Patient Safety Creativeness ab: 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. pubtype: Academic Journal doctype: tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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