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Artificial intelligence in oncology, its scope and future prospects with specific reference to radiation oncology
OBJECTIVE: Artificial intelligence (AI) seems to be bridging the gap between the acquisition of data and its meaningful interpretation. These approaches, have shown outstanding capabilities, outperforming most classification and regression methods to date and the ability to automatically learn the m...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
The British Institute of Radiology.
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7592433/ https://www.ncbi.nlm.nih.gov/pubmed/33178922 http://dx.doi.org/10.1259/bjro.20180031 |
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author | Rattan, Rajit Kataria, Tejinder Banerjee, Susovan Goyal, Shikha Gupta, Deepak Pandita, Akshi Bisht, Shyam Narang, Kushal Mishra, Saumya Ranjan |
author_facet | Rattan, Rajit Kataria, Tejinder Banerjee, Susovan Goyal, Shikha Gupta, Deepak Pandita, Akshi Bisht, Shyam Narang, Kushal Mishra, Saumya Ranjan |
author_sort | Rattan, Rajit |
collection | PubMed |
description | OBJECTIVE: Artificial intelligence (AI) seems to be bridging the gap between the acquisition of data and its meaningful interpretation. These approaches, have shown outstanding capabilities, outperforming most classification and regression methods to date and the ability to automatically learn the most suitable data representation for the task at hand and present it for better correlation. This article tries to sensitize the practising radiation oncologists to understand where the potential role of AI lies and what further can be achieved with it. METHODS AND MATERIALS: Contemporary literature was searched and the available literature was sorted and an attempt at writing a comprehensive non-systematic review was made. RESULTS: The article addresses various areas in oncology, especially in the field of radiation oncology, where the work based on AI has been done. Whether it’s the screening modalities, or diagnosis or the prognostic assays, AI has come with more accurately defining results and survival of patients. Various steps and protocols in radiation oncology are now using AI-based methods, like in the steps of planning, segmentation and delivery of radiation. Benefit of AI across all the platforms of health sector may lead to a more refined and personalized medicine in near future. CONCLUSION: AI with the use of machine learning and artificial neural networks has come up with faster and more accurate solutions for the problems faced by oncologist. The uses of AI,are likely to get increased exponentially . However, concerns regarding demographic discrepancies in relation to patients, disease and their natural history and reports of manipulation of AI, the ultimate responsibility will rest on the treating physicians. |
format | Online Article Text |
id | pubmed-7592433 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | The British Institute of Radiology. |
record_format | MEDLINE/PubMed |
spelling | pubmed-75924332020-11-10 Artificial intelligence in oncology, its scope and future prospects with specific reference to radiation oncology Rattan, Rajit Kataria, Tejinder Banerjee, Susovan Goyal, Shikha Gupta, Deepak Pandita, Akshi Bisht, Shyam Narang, Kushal Mishra, Saumya Ranjan BJR Open Review Article OBJECTIVE: Artificial intelligence (AI) seems to be bridging the gap between the acquisition of data and its meaningful interpretation. These approaches, have shown outstanding capabilities, outperforming most classification and regression methods to date and the ability to automatically learn the most suitable data representation for the task at hand and present it for better correlation. This article tries to sensitize the practising radiation oncologists to understand where the potential role of AI lies and what further can be achieved with it. METHODS AND MATERIALS: Contemporary literature was searched and the available literature was sorted and an attempt at writing a comprehensive non-systematic review was made. RESULTS: The article addresses various areas in oncology, especially in the field of radiation oncology, where the work based on AI has been done. Whether it’s the screening modalities, or diagnosis or the prognostic assays, AI has come with more accurately defining results and survival of patients. Various steps and protocols in radiation oncology are now using AI-based methods, like in the steps of planning, segmentation and delivery of radiation. Benefit of AI across all the platforms of health sector may lead to a more refined and personalized medicine in near future. CONCLUSION: AI with the use of machine learning and artificial neural networks has come up with faster and more accurate solutions for the problems faced by oncologist. The uses of AI,are likely to get increased exponentially . However, concerns regarding demographic discrepancies in relation to patients, disease and their natural history and reports of manipulation of AI, the ultimate responsibility will rest on the treating physicians. The British Institute of Radiology. 2019-05-13 /pmc/articles/PMC7592433/ /pubmed/33178922 http://dx.doi.org/10.1259/bjro.20180031 Text en © 2019 The Authors. Published by the British Institute of Radiology This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Review Article Rattan, Rajit Kataria, Tejinder Banerjee, Susovan Goyal, Shikha Gupta, Deepak Pandita, Akshi Bisht, Shyam Narang, Kushal Mishra, Saumya Ranjan Artificial intelligence in oncology, its scope and future prospects with specific reference to radiation oncology |
title | Artificial intelligence in oncology, its scope and future prospects with specific reference to radiation oncology |
title_full | Artificial intelligence in oncology, its scope and future prospects with specific reference to radiation oncology |
title_fullStr | Artificial intelligence in oncology, its scope and future prospects with specific reference to radiation oncology |
title_full_unstemmed | Artificial intelligence in oncology, its scope and future prospects with specific reference to radiation oncology |
title_short | Artificial intelligence in oncology, its scope and future prospects with specific reference to radiation oncology |
title_sort | artificial intelligence in oncology, its scope and future prospects with specific reference to radiation oncology |
topic | Review Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7592433/ https://www.ncbi.nlm.nih.gov/pubmed/33178922 http://dx.doi.org/10.1259/bjro.20180031 |
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