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Clinical applications of artificial intelligence and machine learning in cancer diagnosis: looking into the future
Artificial intelligence (AI) is the use of mathematical algorithms to mimic human cognitive abilities and to address difficult healthcare challenges including complex biological abnormalities like cancer. The exponential growth of AI in the last decade is evidenced to be the potential platform for o...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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BioMed Central
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8139146/ https://www.ncbi.nlm.nih.gov/pubmed/34020642 http://dx.doi.org/10.1186/s12935-021-01981-1 |
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author | Iqbal, Muhammad Javed Javed, Zeeshan Sadia, Haleema Qureshi, Ijaz A. Irshad, Asma Ahmed, Rais Malik, Kausar Raza, Shahid Abbas, Asif Pezzani, Raffaele Sharifi-Rad, Javad |
author_facet | Iqbal, Muhammad Javed Javed, Zeeshan Sadia, Haleema Qureshi, Ijaz A. Irshad, Asma Ahmed, Rais Malik, Kausar Raza, Shahid Abbas, Asif Pezzani, Raffaele Sharifi-Rad, Javad |
author_sort | Iqbal, Muhammad Javed |
collection | PubMed |
description | Artificial intelligence (AI) is the use of mathematical algorithms to mimic human cognitive abilities and to address difficult healthcare challenges including complex biological abnormalities like cancer. The exponential growth of AI in the last decade is evidenced to be the potential platform for optimal decision-making by super-intelligence, where the human mind is limited to process huge data in a narrow time range. Cancer is a complex and multifaced disorder with thousands of genetic and epigenetic variations. AI-based algorithms hold great promise to pave the way to identify these genetic mutations and aberrant protein interactions at a very early stage. Modern biomedical research is also focused to bring AI technology to the clinics safely and ethically. AI-based assistance to pathologists and physicians could be the great leap forward towards prediction for disease risk, diagnosis, prognosis, and treatments. Clinical applications of AI and Machine Learning (ML) in cancer diagnosis and treatment are the future of medical guidance towards faster mapping of a new treatment for every individual. By using AI base system approach, researchers can collaborate in real-time and share knowledge digitally to potentially heal millions. In this review, we focused to present game-changing technology of the future in clinics, by connecting biology with Artificial Intelligence and explain how AI-based assistance help oncologist for precise treatment. |
format | Online Article Text |
id | pubmed-8139146 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-81391462021-05-25 Clinical applications of artificial intelligence and machine learning in cancer diagnosis: looking into the future Iqbal, Muhammad Javed Javed, Zeeshan Sadia, Haleema Qureshi, Ijaz A. Irshad, Asma Ahmed, Rais Malik, Kausar Raza, Shahid Abbas, Asif Pezzani, Raffaele Sharifi-Rad, Javad Cancer Cell Int Review Artificial intelligence (AI) is the use of mathematical algorithms to mimic human cognitive abilities and to address difficult healthcare challenges including complex biological abnormalities like cancer. The exponential growth of AI in the last decade is evidenced to be the potential platform for optimal decision-making by super-intelligence, where the human mind is limited to process huge data in a narrow time range. Cancer is a complex and multifaced disorder with thousands of genetic and epigenetic variations. AI-based algorithms hold great promise to pave the way to identify these genetic mutations and aberrant protein interactions at a very early stage. Modern biomedical research is also focused to bring AI technology to the clinics safely and ethically. AI-based assistance to pathologists and physicians could be the great leap forward towards prediction for disease risk, diagnosis, prognosis, and treatments. Clinical applications of AI and Machine Learning (ML) in cancer diagnosis and treatment are the future of medical guidance towards faster mapping of a new treatment for every individual. By using AI base system approach, researchers can collaborate in real-time and share knowledge digitally to potentially heal millions. In this review, we focused to present game-changing technology of the future in clinics, by connecting biology with Artificial Intelligence and explain how AI-based assistance help oncologist for precise treatment. BioMed Central 2021-05-21 /pmc/articles/PMC8139146/ /pubmed/34020642 http://dx.doi.org/10.1186/s12935-021-01981-1 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Review Iqbal, Muhammad Javed Javed, Zeeshan Sadia, Haleema Qureshi, Ijaz A. Irshad, Asma Ahmed, Rais Malik, Kausar Raza, Shahid Abbas, Asif Pezzani, Raffaele Sharifi-Rad, Javad Clinical applications of artificial intelligence and machine learning in cancer diagnosis: looking into the future |
title | Clinical applications of artificial intelligence and machine learning in cancer diagnosis: looking into the future |
title_full | Clinical applications of artificial intelligence and machine learning in cancer diagnosis: looking into the future |
title_fullStr | Clinical applications of artificial intelligence and machine learning in cancer diagnosis: looking into the future |
title_full_unstemmed | Clinical applications of artificial intelligence and machine learning in cancer diagnosis: looking into the future |
title_short | Clinical applications of artificial intelligence and machine learning in cancer diagnosis: looking into the future |
title_sort | clinical applications of artificial intelligence and machine learning in cancer diagnosis: looking into the future |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8139146/ https://www.ncbi.nlm.nih.gov/pubmed/34020642 http://dx.doi.org/10.1186/s12935-021-01981-1 |
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