FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W3081359095> ?p ?o ?g. }

• W3081359095 endingPage "S115" @default.
• W3081359095 startingPage "S114" @default.
• W3081359095 abstract "“By three methods we may learn wisdom: First, by reflection, which is noblest; Second, by imitation, which is easiest; and third by experience, which is the bitterest.” Quote by Confucious. Artificial Intelligence (AI) is increasingly used in radiation oncology and predicted to contribute significantly to oncology workflow.1Thompson R.F. Valdes G. Fuller C.D. et al.Artificial intelligence in radiation oncology: a specialty-wide disruptive transformation?.Radiother Oncol. 2018; 129: 421-426Abstract Full Text Full Text PDF PubMed Scopus (127) Google Scholar,2Boon I.S. Au Yong T.P.T. Boon C.S. Assessing the role of artificial intelligence (AI) in clinical oncology: utility of machine learning in radiotherapy target volume delineation.Medicines (Basel). 2018; 5: 131Crossref Google Scholar The current COVID-19 pandemic will likely shift the urgency towards adaptation of AI into oncology to possibly mitigate increased workload and reduced workforce. As such, all members of the radiation oncology community from clinicians, radiographers, dosimetrist and physicists must be able to understand and appraise the evidence of machine learning approaches.3Murphy A. Liszewski B. Artificial intelligence and the medical radiation profession: how our advocacy must inform future practice.J Med Imaging Radiat Sci. 2019; 50: S15-S19Abstract Full Text Full Text PDF PubMed Scopus (16) Google Scholar,4Vollmer S. Mateen B.A. Bohner G. et al.Machine learning and artificial intelligence research for patient benefit: 20 critical questions on transparency, replicability, ethics, and effectiveness.BMJ. 2020; 368: l6927Crossref PubMed Scopus (151) Google Scholar Oncology practice changes have always been led by evidence-based medicine. Therefore, AI must be validated and demonstrate efficacy prior to application into clinical practice. Machine learning algorithms require access to quality medical data to train their algorithms.2Boon I.S. Au Yong T.P.T. Boon C.S. Assessing the role of artificial intelligence (AI) in clinical oncology: utility of machine learning in radiotherapy target volume delineation.Medicines (Basel). 2018; 5: 131Crossref Google Scholar Clinical trial participants have been shown to be keen for their data to contribute to medical advancements.5Mello M.M. Lieou V. Goodman S.N. Clinical trial participants' views of the risks and benefits of data sharing.N Engl J Med. 2018; 378: 2202-2211Crossref PubMed Scopus (108) Google Scholar Therefore, access to quality oncology data should be made available with establishment of open-access national data bank to propel developments of machine learning algorithms.6Boon I.S. Au Yong Boon C.S. Application of artificial intelligence (AI) in radiotherapy workflow: paradigm shift in precision radiotherapy using machine learning.Br J Radiol. 2019; 92: 20190716Crossref PubMed Scopus (4) Google Scholar Smart use of oncology data is essential to ensure computational methods developed are truly translatable and reproducible and not dataset cohort specific. AI methodology developed must be transparent and explainable to clinicians. This is essential so that clinical assumptions input into machine learning algorithms and resultant clinical decisions outputs can be appraised by clinicians.2Boon I.S. Au Yong T.P.T. Boon C.S. Assessing the role of artificial intelligence (AI) in clinical oncology: utility of machine learning in radiotherapy target volume delineation.Medicines (Basel). 2018; 5: 131Crossref Google Scholar The final clinical decision making and responsibility still lies with responsible clinicians. The success of AI in precision oncology can only be gained by genuine collaboration across specialties.2Boon I.S. Au Yong T.P.T. Boon C.S. Assessing the role of artificial intelligence (AI) in clinical oncology: utility of machine learning in radiotherapy target volume delineation.Medicines (Basel). 2018; 5: 131Crossref Google Scholar,6Boon I.S. Au Yong Boon C.S. Application of artificial intelligence (AI) in radiotherapy workflow: paradigm shift in precision radiotherapy using machine learning.Br J Radiol. 2019; 92: 20190716Crossref PubMed Scopus (4) Google Scholar The importance is not only for oncologists to understand the language of machine learning but also for computer scientists and collaborators to understand the unmet oncology needs and holistic understanding of oncology workflow for efficient adaptation of AI to oncology care, which offers clinical value and improve cancer outcomes. Oncology decision-making are often life changing and should be made with transparency, ethics and engagement of clinician and patients. Therefore, soft skills such as emotional intelligence, communication skills, compassion, empathy and leadership skills are essential to the oncology community.7Abu Awwad D. Lewis S.J. Mackay S. Robinson J. Examining the relationship between emotional intelligence, leadership attributes and workplace experience of Australian chief radiographers.J Med Imaging Radiat Sci. 2020; 51: 256-263Abstract Full Text Full Text PDF PubMed Scopus (2) Google Scholar Such soft skills cannot be automated but is pivotal to deliver quality oncology care and can often be difficult to quantify and measure. AI and emotional intelligence are both powerful and important skills for the delivery of excellent oncology care. Radiation oncology community must develop new ways to understand, master and utilise these skills to the best of our ability to benefit our patients.8Chamunyonga C. Edwards C. Caldwell P. Rutledge P. Burbery J. The impact of artificial intelligence and machine learning in radiation therapy: considerations for future curriculum enhancement.J Med Imaging Radiat Sci. 2020; 51: 214-220Abstract Full Text Full Text PDF PubMed Scopus (22) Google Scholar Patients’ voice matters and must be heard. This is ever more important in the climate of uncertainty brought by the COVID-19 pandemic. In such times, our patients offer invaluable and important insights and lessons to radiation oncology practitioners. Patients continue to treasure the human interaction with practitioners.9Gaetz L. Perspective from a patient partner.J Med Imaging Radiat Sci. 2020; https://doi.org/10.1016/j.jmir.2020.06.005Abstract Full Text Full Text PDF PubMed Scopus (2) Google Scholar Compassion, empathy and good communications cannot be delivered by AI and defines the basis of good patient care. Essentially, each clinician represents human (clinician) intelligence providing an independent ground truth for machine learning approaches.6Boon I.S. Au Yong Boon C.S. Application of artificial intelligence (AI) in radiotherapy workflow: paradigm shift in precision radiotherapy using machine learning.Br J Radiol. 2019; 92: 20190716Crossref PubMed Scopus (4) Google Scholar This is analogous to machine learning AI algorithms. Clinicians in training with increasing experience and exposure will gain more knowledge. Similarly, AI given data and training time will gain experience in arranging inputs to produce an output. Applying this to clinical practice and oncological decision-making, clinicians ultimately must need to make final decision and justify the treatment options in discussion with patients to arrive at a final decision. Therefore, soft skills cannot be replaced by AI completely. However, AI may complement and be synergistic to training and development of soft skills by making the oncology workflow more efficient and therefore more time available to develop and hone such essential soft skills. Practitioners armed with excellent compassion, soft skills and complemented with judicious and wise use of AI will represent the future of radiation oncology." @default.
• W3081359095 created "2020-09-01" @default.
• W3081359095 creator A5002322688 @default.
• W3081359095 creator A5013137537 @default.
• W3081359095 creator A5022976515 @default.
• W3081359095 creator A5035901116 @default.
• W3081359095 creator A5037771946 @default.
• W3081359095 date "2020-12-01" @default.
• W3081359095 modified "2023-09-25" @default.
• W3081359095 title "Artificial intelligence and soft skills in radiation oncology: Data versus wisdom" @default.
• W3081359095 cites W2806876255 @default.
• W3081359095 cites W2808391149 @default.
• W3081359095 cites W2905395632 @default.
• W3081359095 cites W2974495841 @default.
• W3081359095 cites W2979834370 @default.
• W3081359095 cites W3007655135 @default.
• W3081359095 cites W3008155962 @default.
• W3081359095 cites W3011403448 @default.
• W3081359095 cites W3040533931 @default.
• W3081359095 doi "https://doi.org/10.1016/j.jmir.2020.08.011" @default.
• W3081359095 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/32859543" @default.
• W3081359095 hasPublicationYear "2020" @default.
• W3081359095 type Work @default.
• W3081359095 sameAs 3081359095 @default.
• W3081359095 citedByCount "6" @default.
• W3081359095 countsByYear W30813590952021 @default.
• W3081359095 countsByYear W30813590952022 @default.
• W3081359095 countsByYear W30813590952023 @default.
• W3081359095 crossrefType "journal-article" @default.
• W3081359095 hasAuthorship W3081359095A5002322688 @default.
• W3081359095 hasAuthorship W3081359095A5013137537 @default.
• W3081359095 hasAuthorship W3081359095A5022976515 @default.
• W3081359095 hasAuthorship W3081359095A5035901116 @default.
• W3081359095 hasAuthorship W3081359095A5037771946 @default.
• W3081359095 hasBestOaLocation W30813590951 @default.
• W3081359095 hasConcept C126322002 @default.
• W3081359095 hasConcept C15744967 @default.
• W3081359095 hasConcept C19527891 @default.
• W3081359095 hasConcept C2992520072 @default.
• W3081359095 hasConcept C43075056 @default.
• W3081359095 hasConcept C509550671 @default.
• W3081359095 hasConcept C509974204 @default.
• W3081359095 hasConcept C71924100 @default.
• W3081359095 hasConceptScore W3081359095C126322002 @default.
• W3081359095 hasConceptScore W3081359095C15744967 @default.
• W3081359095 hasConceptScore W3081359095C19527891 @default.
• W3081359095 hasConceptScore W3081359095C2992520072 @default.
• W3081359095 hasConceptScore W3081359095C43075056 @default.
• W3081359095 hasConceptScore W3081359095C509550671 @default.
• W3081359095 hasConceptScore W3081359095C509974204 @default.
• W3081359095 hasConceptScore W3081359095C71924100 @default.
• W3081359095 hasIssue "4" @default.
• W3081359095 hasLocation W30813590951 @default.
• W3081359095 hasOpenAccess W3081359095 @default.
• W3081359095 hasPrimaryLocation W30813590951 @default.
• W3081359095 hasRelatedWork W160538095 @default.
• W3081359095 hasRelatedWork W2039595483 @default.
• W3081359095 hasRelatedWork W2133747887 @default.
• W3081359095 hasRelatedWork W2153040865 @default.
• W3081359095 hasRelatedWork W2416525736 @default.
• W3081359095 hasRelatedWork W2469123345 @default.
• W3081359095 hasRelatedWork W2975836687 @default.
• W3081359095 hasRelatedWork W3031133862 @default.
• W3081359095 hasRelatedWork W4213322701 @default.
• W3081359095 hasRelatedWork W2039805290 @default.
• W3081359095 hasVolume "51" @default.
• W3081359095 isParatext "false" @default.
• W3081359095 isRetracted "false" @default.
• W3081359095 magId "3081359095" @default.
• W3081359095 workType "article" @default.