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International Conference on Applied Computing to Support Industry: Innovation and Technology

ACRIT 2019: Applied Computing to Support Industry: Innovation and Technology pp 248–261Cite as

The Application of Artificial Intelligence Technology in Healthcare: A Systematic Review

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1174))

Abstract

The proliferation of artificial intelligence and its continued development can be attributed to the pursuit of advanced machine learning techniques for handling big health data. Even though AI appears to be an independent system while considering algorithms and learning techniques, it, however, requires integration of different machine learning algorithms to enable it to handle different data structures. Notably, the number of articles addressing AI implementation from a medical research perspective are on the rise. Further, AI in medical research have machine learning component and as such relies on algorithms such as support vector machine, neural network, deep learning, and convolution neural networks. Of these algorithms, support vector machine is the most commonly used, and it has been applied in medical imaging, diagnosis and treatment of stroke as well as early detection of cancer and neurology conditions. As per the survey, AI results in higher accuracy of diagnosis and risk prediction compared to human approaches. Despite such success and promising future, AI faces regulatory and data related challenges.

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References

  1. Jiang, F., et al.: Artificial intelligence in healthcare: past, present and future. Stroke Vasc. Neurol. 2(4), 230–243 (2017)

    Article  Google Scholar 

  2. Hamet, P., Tremblay, J.: Artificial intelligence in medicine. Metabolism 69, S36–S40 (2017)

    Article  Google Scholar 

  3. Sikchi, S.S., Sikchi, S., Ali, M.S.: Artificial intelligence in medical diagnosis. Int. J. Appl. Eng. Res. 7, 2012 (2012)

    Google Scholar 

  4. Amato, F., López, A., Peña-Méndez, E.M., Vaňhara, P., Hampl, A., Havel, J.: Artificial neural networks in medical diagnosis. J. Appl. Biomed. 11, 47–58 (2013)

    Article  Google Scholar 

  5. Hosny, A., Parmar, C., Quackenbush, J., Schwartz, L.H., Aerts, H.J.W.L.: Artificial intelligence in radiology. Nat. Rev. Cancer 18, 500–510 (2018)

    Article  Google Scholar 

  6. Jiang, J., Trundle, P., Ren, J.: Medical image analysis with artificial neural networks. Comput. Med. Imaging Graph. 34, 617–631 (2010)

    Article  Google Scholar 

  7. Ganapathy, K., Abdul, S.S., Nursetyo, A.A., et al.: Artificial intelligence in neurosciences: a clinician’s perspective. Neurol. India 66(4), 934 (2018)

    Article  Google Scholar 

  8. Ravi, D., et al.: Deep learning for health informatics. IEEE J. Biomed. Health Inform. 21, 4–21 (2017)

    Article  Google Scholar 

  9. Pannu, A.: Artificial intelligence and its application in different areas. Int. J. Eng. Innov. Technol. 4, 79–84 (2015)

    Google Scholar 

  10. Hashimoto, D.A., Rosman, G., Rus, D., Meireles, O.R.: Artificial intelligence in surgery. Ann. Surg. 268, 70–76 (2018)

    Article  Google Scholar 

  11. Johnson, K.W., et al.: Artificial intelligence in cardiology. J. Am. Coll. Cardiol. 71(23), 2668–2679 (2018)

    Article  Google Scholar 

  12. Jha, S., Topol, E.J.: Adapting to artificial intelligence: radiologists and pathologists as information specialists. JAMA – J. Am. Med. Assoc. 316, 2353–2354 (2016)

    Article  Google Scholar 

  13. Lu, H., Li, Y., Chen, M., Kim, H., Serikawa, S.: Brain intelligence: go beyond artificial intelligence. Mob. Netw. Appl. 23, 368–375 (2018)

    Article  Google Scholar 

  14. Miller, D.D., Brown, E.W.: Artificial intelligence in medical practice: the question to the answer? Am. J. Med. 131, 129–133 (2018)

    Article  Google Scholar 

  15. Giger, M.L.: Machine learning in medical imaging. J. Am. Coll. Radiol. 15, 512–520 (2018)

    Article  Google Scholar 

  16. Deepa, S.N., Aruna Devi, B.: A survey on artificial intelligence approaches for medical image classification. Indian J. Sci. Technol. 4, 1583–1595 (2011)

    Google Scholar 

  17. Acampora, G., Cook, D.J., Rashidi, P., Vasilakos, A.V.: A survey on ambient intelligence in healthcare. Proc. IEEE 101, 2470–2494 (2013)

    Article  Google Scholar 

  18. Nemati, S., Holder, A., Razmi, F., Stanley, M.D., Clifford, G.D., Buchman, T.G.: An interpretable machine learning model for accurate prediction of sepsis in the ICU. Crit. Care Med. 46, 547–553 (2017)

    Article  Google Scholar 

  19. Liu, C., Sun, W., Chao, W., Che, W.: Convolution neural network for relation extraction. In: Motoda, H., Wu, Z., Cao, L., Zaiane, O., Yao, M., Wang, W. (eds.) ADMA 2013. LNCS (LNAI), vol. 8347, pp. 231–242. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-53917-6_21

    Chapter  Google Scholar 

  20. Jin, K.H., McCann, M.T., Froustey, E., Unser, M.: Deep convolutional neural network for inverse problems in imaging. IEEE Trans. Image Process. 26, 4509–4522 (2017)

    Article  MathSciNet  Google Scholar 

  21. Hengstler, M., Enkel, E., Duelli, S.: Applied artificial intelligence and trust-the case of autonomous vehicles and medical assistance devices. Technol. Forecast. Soc. Change 105, 105–120 (2016)

    Article  Google Scholar 

  22. Beam, A.L., Kohane, I.S.: Translating artificial intelligence into clinical care. JAMA – J. Am. Med. Assoc. 316, 2368–2369 (2016)

    Article  Google Scholar 

  23. Katan, M., Luft, A.: Global burden of stroke. Semin. Neurol. 38, 208–211 (2018)

    Article  Google Scholar 

  24. Bhatnagar, S., et al.: The Malicious Use of Artificial Intelligence: Forecasting, Prevention, and Mitigation Authors are listed in order of contribution Design Direction. CoRR (2018)

    Google Scholar 

  25. Pang, Y., Sun, M., Jiang, X., Li, X.: Convolution in convolution for network in network. IEEE Trans. Neural Netw. Learn. Syst. 29, 1587–1597 (2018)

    Article  MathSciNet  Google Scholar 

  26. Davenport, T.H., Ronanki, R.: Artificial intelligence for the real world. Harv. Bus. Rev. 96, 108–116 (2018)

    Google Scholar 

  27. Ibrahim, F., Thio, T.H.G., Faisal, T., Neuman, M.: The application of biomedical engineering techniques to the diagnosis and management of tropical diseases: a review. Sensors (Switzerland) 15, 6947–6995 (2015)

    Article  Google Scholar 

  28. Labovitz, D.L., Shafner, L., Reyes Gil, M., Virmani, D., Hanina, A.: Using artificial intelligence to reduce the risk of nonadherence in patients on anticoagulation therapy. Stroke 48, 1416–1419 (2017)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Liverpool John Moores University, Liverpool, L3 3AF, UK

    Mohamed Alloghani, Dhiya Al-Jumeily, Ahmed J. Aljaaf & Sin Y. Tan

  2. Abu Dhabi Health Services Company (SEHA), Abu Dhabi, UAE

    Mohamed Alloghani

  3. Centre of Computer, University of Anbar, Ramadi, Iraq

    Ahmed J. Aljaaf

  4. Department of Computer Science, Almaarif University College, Ramadi, Anbar, Iraq

    Mohammed Khalaf

  5. Kazan Federal University, Kazan, Russia

    Jamila Mustafina

Authors
  1. Mohamed Alloghani
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  2. Dhiya Al-Jumeily
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  3. Ahmed J. Aljaaf
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  4. Mohammed Khalaf
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  5. Jamila Mustafina
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  6. Sin Y. Tan
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Corresponding author

Correspondence to Mohamed Alloghani .

Editor information

Editors and Affiliations

  1. Al Maaref University College, Ramadi, Iraq

    Mohammed I. Khalaf

  2. Liverpool John Moores University, Liverpool, UK

    Dhiya Al-Jumeily

  3. University of Liverpool, Liverpool, UK

    Alexei Lisitsa

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Alloghani, M., Al-Jumeily, D., Aljaaf, A.J., Khalaf, M., Mustafina, J., Tan, S.Y. (2020). The Application of Artificial Intelligence Technology in Healthcare: A Systematic Review. In: Khalaf, M., Al-Jumeily, D., Lisitsa, A. (eds) Applied Computing to Support Industry: Innovation and Technology. ACRIT 2019. Communications in Computer and Information Science, vol 1174. Springer, Cham. https://doi.org/10.1007/978-3-030-38752-5_20

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  • DOI: https://doi.org/10.1007/978-3-030-38752-5_20

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-38751-8

  • Online ISBN: 978-3-030-38752-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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