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Deep learning technology for improving cancer care in society: New directions in cancer imaging driven by artificial intelligence

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  • Coccia, Mario

Abstract

The goal of this study is to show emerging applications of deep learning technology in cancer imaging. Deep learning technology is a family of computational methods that allow an algorithm to program itself by learning from a large set of examples that demonstrate the desired behavior. Applications of deep learning technology to cancer imaging can assist pathologists in the detection and classification of cancer in the early stages of its development to allow patients to have appropriate treatments that can increase their survival. Statistical analyses and other analytical approaches, based on data of ScienceDirect (a source for scientific research), suggest that the sharp increase of the studies of deep learning technology in cancer imaging seems to be driven by high rates of mortality of some types of cancer (e.g., lung and breast) in order to solve consequential problems of a more accurate detection and characterization of cancer types to apply efficient anti-cancer therapies. Moreover, this study also shows sources of the trajectories of deep learning technology in cancer imaging at level of scientific subject areas, universities and countries with the highest scientific production in these research fields. This new technology, in accordance with Amara's law, can generate a shift of technological paradigm for diagnostic assessment of any cancer type and disease. This new technology can also generate socioeconomic benefits for poor regions because they can send digital images to labs of other developed regions to have diagnosis of cancer types, reducing as far as possible current gap in healthcare sector among different regions.

Suggested Citation

  • Coccia, Mario, 2020. "Deep learning technology for improving cancer care in society: New directions in cancer imaging driven by artificial intelligence," Technology in Society, Elsevier, vol. 60(C).
    • Handle: RePEc:eee:teinso:v:60:y:2020:i:c:s0160791x1930274x
    DOI: 10.1016/j.techsoc.2019.101198
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    References listed on IDEAS

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    1. Coccia, Mario, 2019. "The theory of technological parasitism for the measurement of the evolution of technology and technological forecasting," Technological Forecasting and Social Change, Elsevier, vol. 141(C), pages 289-304.
    2. Mario Coccia, 2018. "General properties of the evolution of research fields: a scientometric study of human microbiome, evolutionary robotics and astrobiology," Scientometrics, Springer;Akadémiai Kiadó, vol. 117(2), pages 1265-1283, November.
    3. Coccia, Mario, 2015. "The Nexus between technological performances of countries and incidence of cancers in society," Technology in Society, Elsevier, vol. 42(C), pages 61-70.
    4. Coccia, Mario & Wang, Lili, 2015. "Path-breaking directions of nanotechnology-based chemotherapy and molecular cancer therapy," Technological Forecasting and Social Change, Elsevier, vol. 94(C), pages 155-169.
    5. Coccia, Mario, 2018. "A Theory of the General Causes of Long Waves: War, General Purpose Technologies, and Economic Change," Technological Forecasting and Social Change, Elsevier, vol. 128(C), pages 287-295.
    6. Coccia, Mario, 2015. "General sources of general purpose technologies in complex societies: Theory of global leadership-driven innovation, warfare and human development," Technology in Society, Elsevier, vol. 42(C), pages 199-226.
    7. Coccia, Mario, 2019. "Why do nations produce science advances and new technology?," Technology in Society, Elsevier, vol. 59(C).
    8. Coccia, Mario, 2012. "Driving forces of technological change in medicine: Radical innovations induced by side effects and their impact on society and healthcare," Technology in Society, Elsevier, vol. 34(4), pages 271-283.
    9. Mario Coccia, 2018. "Optimization in R&D intensity and tax on corporate profits for supporting labor productivity of nations," The Journal of Technology Transfer, Springer, vol. 43(3), pages 792-814, June.
    10. Wright, Gavin, 1997. "Towards a More Historical Approach to Technological Change," Economic Journal, Royal Economic Society, vol. 107(444), pages 1560-1566, September.
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    Citations

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    Cited by:

    1. Mario Coccia, 2020. "The evolution of scientific disciplines in applied sciences: dynamics and empirical properties of experimental physics," Scientometrics, Springer;Akadémiai Kiadó, vol. 124(1), pages 451-487, July.
    2. Mario Coccia, 2020. "How ( Un )sustainable Environments Are Related to the Diffusion of COVID-19: The Relation between Coronavirus Disease 2019, Air Pollution, Wind Resource and Energy," Sustainability, MDPI, vol. 12(22), pages 1-12, November.
    3. Chuan-Shen Hu & Austin Lawson & Jung-Sheng Chen & Yu-Min Chung & Clifford Smyth & Shih-Min Yang, 2021. "TopoResNet: A Hybrid Deep Learning Architecture and Its Application to Skin Lesion Classification," Mathematics, MDPI, vol. 9(22), pages 1-22, November.
    4. Kulkov, Ignat, 2021. "The role of artificial intelligence in business transformation: A case of pharmaceutical companies," Technology in Society, Elsevier, vol. 66(C).
    5. Lee, Changyong, 2021. "A review of data analytics in technological forecasting," Technological Forecasting and Social Change, Elsevier, vol. 166(C).
    6. Trivedi, Shrawan Kumar, 2020. "A study on credit scoring modeling with different feature selection and machine learning approaches," Technology in Society, Elsevier, vol. 63(C).
    7. Ostheimer, Julia & Chowdhury, Soumitra & Iqbal, Sarfraz, 2021. "An alliance of humans and machines for machine learning: Hybrid intelligent systems and their design principles," Technology in Society, Elsevier, vol. 66(C).
    8. Shabnam Gul & Muhammad Faizan Asghar & Adeel Irfan, 2020. "Artificial Intelligence as the New Art of War: An Appraisal," Global Regional Review, Humanity Only, vol. 5(1), pages 642-650, March.

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    More about this item

    Keywords

    Deep learning; Cancer imaging; Artificial intelligence; Lung cancer; Breast cancer; Technological paradigm; Amara's law; Gartner hype cycle; Emerging technology; New technology;
    All these keywords.

    JEL classification:

    • O32 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Management of Technological Innovation and R&D
    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes

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