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Translating Data Science Results into Precision Oncology Decisions: A Mini Review
While reviewing and discussing the potential of data science in oncology, we emphasize medical imaging and radiomics as the leading contextual frameworks to measure the impacts of Artificial Intelligence (AI) and Machine Learning (ML) developments. We envision some domains and research directions in...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9862106/ https://www.ncbi.nlm.nih.gov/pubmed/36675367 http://dx.doi.org/10.3390/jcm12020438 |
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author | Capobianco, Enrico Dominietto, Marco |
author_facet | Capobianco, Enrico Dominietto, Marco |
author_sort | Capobianco, Enrico |
collection | PubMed |
description | While reviewing and discussing the potential of data science in oncology, we emphasize medical imaging and radiomics as the leading contextual frameworks to measure the impacts of Artificial Intelligence (AI) and Machine Learning (ML) developments. We envision some domains and research directions in which radiomics should become more significant in view of current barriers and limitations. |
format | Online Article Text |
id | pubmed-9862106 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-98621062023-01-22 Translating Data Science Results into Precision Oncology Decisions: A Mini Review Capobianco, Enrico Dominietto, Marco J Clin Med Review While reviewing and discussing the potential of data science in oncology, we emphasize medical imaging and radiomics as the leading contextual frameworks to measure the impacts of Artificial Intelligence (AI) and Machine Learning (ML) developments. We envision some domains and research directions in which radiomics should become more significant in view of current barriers and limitations. MDPI 2023-01-05 /pmc/articles/PMC9862106/ /pubmed/36675367 http://dx.doi.org/10.3390/jcm12020438 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Review Capobianco, Enrico Dominietto, Marco Translating Data Science Results into Precision Oncology Decisions: A Mini Review |
title | Translating Data Science Results into Precision Oncology Decisions: A Mini Review |
title_full | Translating Data Science Results into Precision Oncology Decisions: A Mini Review |
title_fullStr | Translating Data Science Results into Precision Oncology Decisions: A Mini Review |
title_full_unstemmed | Translating Data Science Results into Precision Oncology Decisions: A Mini Review |
title_short | Translating Data Science Results into Precision Oncology Decisions: A Mini Review |
title_sort | translating data science results into precision oncology decisions: a mini review |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9862106/ https://www.ncbi.nlm.nih.gov/pubmed/36675367 http://dx.doi.org/10.3390/jcm12020438 |
work_keys_str_mv | AT capobiancoenrico translatingdatascienceresultsintoprecisiononcologydecisionsaminireview AT dominiettomarco translatingdatascienceresultsintoprecisiononcologydecisionsaminireview |