File Download
  Links for fulltext
     (May Require Subscription)

Article: Current landscape and potential future applications of artificial intelligence in medical physics and radiotherapy

TitleCurrent landscape and potential future applications of artificial intelligence in medical physics and radiotherapy
Authors
KeywordsArtificial intelligence
Medical physics
Radiotherapy
Image acquisition
Image segmentation
Issue Date2021
PublisherBaishideng Publishing Group Co., Limited. The Journal's web site is located at https://www.wjgnet.com/2644-3260/index.htm
Citation
Artificial Intelligence in Medical Imaging, 2021, v. 2 n. 2, p. 37-55 How to Cite?
AbstractArtificial intelligence (AI) has seen tremendous growth over the past decade and stands to disrupts the medical industry. In medicine, this has been applied in medical imaging and other digitised medical disciplines, but in more traditional fields like medical physics, the adoption of AI is still at an early stage. Though AI is anticipated to be better than human in certain tasks, with the rapid growth of AI, there is increasing concerns for its usage. The focus of this paper is on the current landscape and potential future applications of artificial intelligence in medical physics and radiotherapy. Topics on AI for image acquisition, image segmentation, treatment delivery, quality assurance and outcome prediction will be explored as well as the interaction between human and AI. This will give insights into how we should approach and use the technology for enhancing the quality of clinical practice.
Persistent Identifierhttp://hdl.handle.net/10722/301228
ISSN

 

DC FieldValueLanguage
dc.contributor.authorIp, WY-
dc.contributor.authorYeung, FK-
dc.contributor.authorYung, SPF-
dc.contributor.authorYu, HCJ-
dc.contributor.authorSo, TH-
dc.contributor.authorVardhanabhuti, V-
dc.date.accessioned2021-07-27T08:08:01Z-
dc.date.available2021-07-27T08:08:01Z-
dc.date.issued2021-
dc.identifier.citationArtificial Intelligence in Medical Imaging, 2021, v. 2 n. 2, p. 37-55-
dc.identifier.issn2644-3260-
dc.identifier.urihttp://hdl.handle.net/10722/301228-
dc.description.abstractArtificial intelligence (AI) has seen tremendous growth over the past decade and stands to disrupts the medical industry. In medicine, this has been applied in medical imaging and other digitised medical disciplines, but in more traditional fields like medical physics, the adoption of AI is still at an early stage. Though AI is anticipated to be better than human in certain tasks, with the rapid growth of AI, there is increasing concerns for its usage. The focus of this paper is on the current landscape and potential future applications of artificial intelligence in medical physics and radiotherapy. Topics on AI for image acquisition, image segmentation, treatment delivery, quality assurance and outcome prediction will be explored as well as the interaction between human and AI. This will give insights into how we should approach and use the technology for enhancing the quality of clinical practice.-
dc.languageeng-
dc.publisherBaishideng Publishing Group Co., Limited. The Journal's web site is located at https://www.wjgnet.com/2644-3260/index.htm-
dc.relation.ispartofArtificial Intelligence in Medical Imaging-
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.-
dc.subjectArtificial intelligence-
dc.subjectMedical physics-
dc.subjectRadiotherapy-
dc.subjectImage acquisition-
dc.subjectImage segmentation-
dc.titleCurrent landscape and potential future applications of artificial intelligence in medical physics and radiotherapy-
dc.typeArticle-
dc.identifier.emailIp, WY: wiip0817@hku.hk-
dc.identifier.emailYung, SPF: spfy@hku.hk-
dc.identifier.emailVardhanabhuti, V: varv@hku.hk-
dc.identifier.authoritySo, TH=rp01981-
dc.identifier.authorityVardhanabhuti, V=rp01900-
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.35711/aimi.v2.i2.37-
dc.identifier.hkuros323407-
dc.identifier.volume2-
dc.identifier.issue2-
dc.identifier.spage37-
dc.identifier.epage55-
dc.publisher.placeUnited States-

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats