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Effectiveness of Artificial Intelligence for Personalized Medicine in Neoplasms: A Systematic Review
PURPOSE: Artificial intelligence (AI) techniques are used in precision medicine to explore novel genotypes and phenotypes data. The main aims of precision medicine include early diagnosis, screening, and personalized treatment regime for a patient based on genetic-oriented features and characteristi...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9010213/ https://www.ncbi.nlm.nih.gov/pubmed/35434134 http://dx.doi.org/10.1155/2022/7842566 |
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author | Rezayi, Sorayya R Niakan Kalhori, Sharareh Saeedi, Soheila |
author_facet | Rezayi, Sorayya R Niakan Kalhori, Sharareh Saeedi, Soheila |
author_sort | Rezayi, Sorayya |
collection | PubMed |
description | PURPOSE: Artificial intelligence (AI) techniques are used in precision medicine to explore novel genotypes and phenotypes data. The main aims of precision medicine include early diagnosis, screening, and personalized treatment regime for a patient based on genetic-oriented features and characteristics. The main objective of this study was to review AI techniques and their effectiveness in neoplasm precision medicine. MATERIALS AND METHODS: A comprehensive search was performed in Medline (through PubMed), Scopus, ISI Web of Science, IEEE Xplore, Embase, and Cochrane databases from inception to December 29, 2021, in order to identify the studies that used AI methods for cancer precision medicine and evaluate outcomes of the models. RESULTS: Sixty-three studies were included in this systematic review. The main AI approaches in 17 papers (26.9%) were linear and nonlinear categories (random forest or decision trees), and in 21 citations, rule-based systems and deep learning models were used. Notably, 62% of the articles were done in the United States and China. R package was the most frequent software, and breast and lung cancer were the most selected neoplasms in the papers. Out of 63 papers, in 34 articles, genomic data like gene expression, somatic mutation data, phenotype data, and proteomics with drug-response which is functional data was used as input in AI methods; in 16 papers' (25.3%) drug response, functional data was utilized in personalization of treatment. The maximum values of the assessment indicators such as accuracy, sensitivity, specificity, precision, recall, and area under the curve (AUC) in included studies were 0.99, 1.00, 0.96, 0.98, 0.99, and 0.9929, respectively. CONCLUSION: The findings showed that in many cases, the use of artificial intelligence methods had effective application in personalized medicine. |
format | Online Article Text |
id | pubmed-9010213 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
spelling | pubmed-90102132022-04-15 Effectiveness of Artificial Intelligence for Personalized Medicine in Neoplasms: A Systematic Review Rezayi, Sorayya R Niakan Kalhori, Sharareh Saeedi, Soheila Biomed Res Int Review Article PURPOSE: Artificial intelligence (AI) techniques are used in precision medicine to explore novel genotypes and phenotypes data. The main aims of precision medicine include early diagnosis, screening, and personalized treatment regime for a patient based on genetic-oriented features and characteristics. The main objective of this study was to review AI techniques and their effectiveness in neoplasm precision medicine. MATERIALS AND METHODS: A comprehensive search was performed in Medline (through PubMed), Scopus, ISI Web of Science, IEEE Xplore, Embase, and Cochrane databases from inception to December 29, 2021, in order to identify the studies that used AI methods for cancer precision medicine and evaluate outcomes of the models. RESULTS: Sixty-three studies were included in this systematic review. The main AI approaches in 17 papers (26.9%) were linear and nonlinear categories (random forest or decision trees), and in 21 citations, rule-based systems and deep learning models were used. Notably, 62% of the articles were done in the United States and China. R package was the most frequent software, and breast and lung cancer were the most selected neoplasms in the papers. Out of 63 papers, in 34 articles, genomic data like gene expression, somatic mutation data, phenotype data, and proteomics with drug-response which is functional data was used as input in AI methods; in 16 papers' (25.3%) drug response, functional data was utilized in personalization of treatment. The maximum values of the assessment indicators such as accuracy, sensitivity, specificity, precision, recall, and area under the curve (AUC) in included studies were 0.99, 1.00, 0.96, 0.98, 0.99, and 0.9929, respectively. CONCLUSION: The findings showed that in many cases, the use of artificial intelligence methods had effective application in personalized medicine. Hindawi 2022-04-07 /pmc/articles/PMC9010213/ /pubmed/35434134 http://dx.doi.org/10.1155/2022/7842566 Text en Copyright © 2022 Sorayya Rezayi et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Review Article Rezayi, Sorayya R Niakan Kalhori, Sharareh Saeedi, Soheila Effectiveness of Artificial Intelligence for Personalized Medicine in Neoplasms: A Systematic Review |
title | Effectiveness of Artificial Intelligence for Personalized Medicine in Neoplasms: A Systematic Review |
title_full | Effectiveness of Artificial Intelligence for Personalized Medicine in Neoplasms: A Systematic Review |
title_fullStr | Effectiveness of Artificial Intelligence for Personalized Medicine in Neoplasms: A Systematic Review |
title_full_unstemmed | Effectiveness of Artificial Intelligence for Personalized Medicine in Neoplasms: A Systematic Review |
title_short | Effectiveness of Artificial Intelligence for Personalized Medicine in Neoplasms: A Systematic Review |
title_sort | effectiveness of artificial intelligence for personalized medicine in neoplasms: a systematic review |
topic | Review Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9010213/ https://www.ncbi.nlm.nih.gov/pubmed/35434134 http://dx.doi.org/10.1155/2022/7842566 |
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