Cargando…

Advancements in Oncology with Artificial Intelligence—A Review Article

SIMPLE SUMMARY: With the advancement of artificial intelligence, including machine learning, the field of oncology has seen promising results in cancer detection and classification, epigenetics, drug discovery, and prognostication. In this review, we describe what artificial intelligence is and its...

Descripción completa

Detalles Bibliográficos
Autores principales: Vobugari, Nikitha, Raja, Vikranth, Sethi, Udhav, Gandhi, Kejal, Raja, Kishore, Surani, Salim R.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8909088/
https://www.ncbi.nlm.nih.gov/pubmed/35267657
http://dx.doi.org/10.3390/cancers14051349
_version_ 1784666033260331008
author Vobugari, Nikitha
Raja, Vikranth
Sethi, Udhav
Gandhi, Kejal
Raja, Kishore
Surani, Salim R.
author_facet Vobugari, Nikitha
Raja, Vikranth
Sethi, Udhav
Gandhi, Kejal
Raja, Kishore
Surani, Salim R.
author_sort Vobugari, Nikitha
collection PubMed
description SIMPLE SUMMARY: With the advancement of artificial intelligence, including machine learning, the field of oncology has seen promising results in cancer detection and classification, epigenetics, drug discovery, and prognostication. In this review, we describe what artificial intelligence is and its function, as well as comprehensively summarize its evolution and role in breast, colorectal, and central nervous system cancers. Understanding the origin and current accomplishments might be essential to improve the quality, accuracy, generalizability, cost-effectiveness, and reliability of artificial intelligence models that can be used in worldwide clinical practice. Students and researchers in the medical field will benefit from a deeper understanding of how to use integrative AI in oncology for innovation and research. ABSTRACT: Well-trained machine learning (ML) and artificial intelligence (AI) systems can provide clinicians with therapeutic assistance, potentially increasing efficiency and improving efficacy. ML has demonstrated high accuracy in oncology-related diagnostic imaging, including screening mammography interpretation, colon polyp detection, glioma classification, and grading. By utilizing ML techniques, the manual steps of detecting and segmenting lesions are greatly reduced. ML-based tumor imaging analysis is independent of the experience level of evaluating physicians, and the results are expected to be more standardized and accurate. One of the biggest challenges is its generalizability worldwide. The current detection and screening methods for colon polyps and breast cancer have a vast amount of data, so they are ideal areas for studying the global standardization of artificial intelligence. Central nervous system cancers are rare and have poor prognoses based on current management standards. ML offers the prospect of unraveling undiscovered features from routinely acquired neuroimaging for improving treatment planning, prognostication, monitoring, and response assessment of CNS tumors such as gliomas. By studying AI in such rare cancer types, standard management methods may be improved by augmenting personalized/precision medicine. This review aims to provide clinicians and medical researchers with a basic understanding of how ML works and its role in oncology, especially in breast cancer, colorectal cancer, and primary and metastatic brain cancer. Understanding AI basics, current achievements, and future challenges are crucial in advancing the use of AI in oncology.
format Online
Article
Text
id pubmed-8909088
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-89090882022-03-11 Advancements in Oncology with Artificial Intelligence—A Review Article Vobugari, Nikitha Raja, Vikranth Sethi, Udhav Gandhi, Kejal Raja, Kishore Surani, Salim R. Cancers (Basel) Review SIMPLE SUMMARY: With the advancement of artificial intelligence, including machine learning, the field of oncology has seen promising results in cancer detection and classification, epigenetics, drug discovery, and prognostication. In this review, we describe what artificial intelligence is and its function, as well as comprehensively summarize its evolution and role in breast, colorectal, and central nervous system cancers. Understanding the origin and current accomplishments might be essential to improve the quality, accuracy, generalizability, cost-effectiveness, and reliability of artificial intelligence models that can be used in worldwide clinical practice. Students and researchers in the medical field will benefit from a deeper understanding of how to use integrative AI in oncology for innovation and research. ABSTRACT: Well-trained machine learning (ML) and artificial intelligence (AI) systems can provide clinicians with therapeutic assistance, potentially increasing efficiency and improving efficacy. ML has demonstrated high accuracy in oncology-related diagnostic imaging, including screening mammography interpretation, colon polyp detection, glioma classification, and grading. By utilizing ML techniques, the manual steps of detecting and segmenting lesions are greatly reduced. ML-based tumor imaging analysis is independent of the experience level of evaluating physicians, and the results are expected to be more standardized and accurate. One of the biggest challenges is its generalizability worldwide. The current detection and screening methods for colon polyps and breast cancer have a vast amount of data, so they are ideal areas for studying the global standardization of artificial intelligence. Central nervous system cancers are rare and have poor prognoses based on current management standards. ML offers the prospect of unraveling undiscovered features from routinely acquired neuroimaging for improving treatment planning, prognostication, monitoring, and response assessment of CNS tumors such as gliomas. By studying AI in such rare cancer types, standard management methods may be improved by augmenting personalized/precision medicine. This review aims to provide clinicians and medical researchers with a basic understanding of how ML works and its role in oncology, especially in breast cancer, colorectal cancer, and primary and metastatic brain cancer. Understanding AI basics, current achievements, and future challenges are crucial in advancing the use of AI in oncology. MDPI 2022-03-06 /pmc/articles/PMC8909088/ /pubmed/35267657 http://dx.doi.org/10.3390/cancers14051349 Text en © 2022 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
Vobugari, Nikitha
Raja, Vikranth
Sethi, Udhav
Gandhi, Kejal
Raja, Kishore
Surani, Salim R.
Advancements in Oncology with Artificial Intelligence—A Review Article
title Advancements in Oncology with Artificial Intelligence—A Review Article
title_full Advancements in Oncology with Artificial Intelligence—A Review Article
title_fullStr Advancements in Oncology with Artificial Intelligence—A Review Article
title_full_unstemmed Advancements in Oncology with Artificial Intelligence—A Review Article
title_short Advancements in Oncology with Artificial Intelligence—A Review Article
title_sort advancements in oncology with artificial intelligence—a review article
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8909088/
https://www.ncbi.nlm.nih.gov/pubmed/35267657
http://dx.doi.org/10.3390/cancers14051349
work_keys_str_mv AT vobugarinikitha advancementsinoncologywithartificialintelligenceareviewarticle
AT rajavikranth advancementsinoncologywithartificialintelligenceareviewarticle
AT sethiudhav advancementsinoncologywithartificialintelligenceareviewarticle
AT gandhikejal advancementsinoncologywithartificialintelligenceareviewarticle
AT rajakishore advancementsinoncologywithartificialintelligenceareviewarticle
AT suranisalimr advancementsinoncologywithartificialintelligenceareviewarticle