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Review
. 2021 May 21;21(1):270.
doi: 10.1186/s12935-021-01981-1.

Clinical applications of artificial intelligence and machine learning in cancer diagnosis: looking into the future

Muhammad Javed Iqbal  1 , Zeeshan Javed  2 , Haleema Sadia  3 , Ijaz A Qureshi  4 , Asma Irshad  5 , Rais Ahmed  6 , Kausar Malik  7 , Shahid Raza  8 , Asif Abbas  1 , Raffaele Pezzani  9   10 , Javad Sharifi-Rad  11   12
Affiliations
Review

Clinical applications of artificial intelligence and machine learning in cancer diagnosis: looking into the future

Muhammad Javed Iqbal et al. Cancer Cell Int. .

Abstract

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.

Keywords: Artificial intelligence; Cancer diagnosis; Machine learning; Therapeutic interventions; Treatment.

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Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Fig. 1
Fig. 1
Applications of AI, ML, and DL in digital health care and oncology to solve healthcare issues and predict optimal treatment outcome
Fig. 2
Fig. 2
Workflow for Artificial Intelligence approach for digital pathology
Fig. 3
Fig. 3
Evolution in digital pathology towards high-resolution graphics and easy storage plus data interpret solutions
Fig. 4
Fig. 4
Figure representing the Artificial Intelligence based clinical data analysis and its healthcare assistance at individual and population level
See this image and copyright information in PMC

References

    1. Jiang F, Jiang Y, Zhi H, Dong Y, Li H, Ma S, Wang Y, Dong Q, Shen H, Wang Y. Artificial intelligence in healthcare: past, present and future. Stroke Vasc Neurol. 2017;2:230–243. doi: 10.1136/svn-2017-000101. - DOI - PMC - PubMed
    1. Wiens J, Shenoy ES. Machine learning for healthcare: on the verge of a major shift in healthcare epidemiology. Clin Infect Dis. 2018;66(1):149–153. doi: 10.1093/cid/cix731. - DOI - PMC - PubMed
    1. Davenport T, Kalakota R. The potential for artificial intelligence in healthcare. Fut Healthcare J. 2019;6(2):94. doi: 10.7861/futurehosp.6-2-94. - DOI - PMC - PubMed
    1. Deng L, Yu D. Deep learning: methods and applications. Found Trends Signal Process. 2014;7(34):197–387. doi: 10.1561/2000000039. - DOI
    1. LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015;521(7553):436–444. doi: 10.1038/nature14539. - DOI - PubMed

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