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Application of Artificial Intelligence-Based Technologies in the Healthcare Industry: Opportunities and Challenges

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  • DonHee Lee

    (College of Business Administration, Inha University, Incheon 22212, Korea)

  • Seong No Yoon

    (Department of Business Edward Waters College, Jacksonville, FL 32209, USA)

Abstract

This study examines the current state of artificial intelligence (AI)-based technology applications and their impact on the healthcare industry. In addition to a thorough review of the literature, this study analyzed several real-world examples of AI applications in healthcare. The results indicate that major hospitals are, at present, using AI-enabled systems to augment medical staff in patient diagnosis and treatment activities for a wide range of diseases. In addition, AI systems are making an impact on improving the efficiency of nursing and managerial activities of hospitals. While AI is being embraced positively by healthcare providers, its applications provide both the utopian perspective (new opportunities) and the dystopian view (challenges to overcome). We discuss the details of those opportunities and challenges to provide a balanced view of the value of AI applications in healthcare. It is clear that rapid advances of AI and related technologies will help care providers create new value for their patients and improve the efficiency of their operational processes. Nevertheless, effective applications of AI will require effective planning and strategies to transform the entire care service and operations to reap the benefits of what technologies offer.

Suggested Citation

  • DonHee Lee & Seong No Yoon, 2021. "Application of Artificial Intelligence-Based Technologies in the Healthcare Industry: Opportunities and Challenges," IJERPH, MDPI, vol. 18(1), pages 1-18, January.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:1:p:271-:d:473475
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    References listed on IDEAS

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    1. Sang M. Lee & DonHee Lee & Youn Sung Kim, 2019. "The quality management ecosystem for predictive maintenance in the Industry 4.0 era," International Journal of Quality Innovation, Springer, vol. 5(1), pages 1-11, December.
    2. Andre Esteva & Brett Kuprel & Roberto A. Novoa & Justin Ko & Susan M. Swetter & Helen M. Blau & Sebastian Thrun, 2017. "Correction: Corrigendum: Dermatologist-level classification of skin cancer with deep neural networks," Nature, Nature, vol. 546(7660), pages 686-686, June.
    3. Sang M. Lee & DonHee Lee, 2020. "Healthcare wearable devices: an analysis of key factors for continuous use intention," Service Business, Springer;Pan-Pacific Business Association, vol. 14(4), pages 503-531, December.
    4. DonHee Lee, 2019. "Effects of key value co-creation elements in the healthcare system: focusing on technology applications," Service Business, Springer;Pan-Pacific Business Association, vol. 13(2), pages 389-417, June.
    5. Andre Esteva & Brett Kuprel & Roberto A. Novoa & Justin Ko & Susan M. Swetter & Helen M. Blau & Sebastian Thrun, 2017. "Dermatologist-level classification of skin cancer with deep neural networks," Nature, Nature, vol. 542(7639), pages 115-118, February.
    6. Lee, DonHee, 2018. "Strategies for technology-driven service encounters for patient experience satisfaction in hospitals," Technological Forecasting and Social Change, Elsevier, vol. 137(C), pages 118-127.
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    Cited by:

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    2. Rupam Singh & Varaha Satya Bharath Kurukuru & Mohammed Ali Khan, 2023. "Advanced Power Converters and Learning in Diverse Robotic Innovation: A Review," Energies, MDPI, vol. 16(20), pages 1-28, October.
    3. Julien Issa & Raphael Olszewski & Marta Dyszkiewicz-Konwińska, 2022. "The Effectiveness of Semi-Automated and Fully Automatic Segmentation for Inferior Alveolar Canal Localization on CBCT Scans: A Systematic Review," IJERPH, MDPI, vol. 19(1), pages 1-10, January.
    4. Zhencheng Fan & Zheng Yan & Shiping Wen, 2023. "Deep Learning and Artificial Intelligence in Sustainability: A Review of SDGs, Renewable Energy, and Environmental Health," Sustainability, MDPI, vol. 15(18), pages 1-20, September.
    5. Nasrin Sultana & Ekaterina Turkina, 2023. "Collaboration for Sustainable Innovation Ecosystem: The Role of Intermediaries," Sustainability, MDPI, vol. 15(10), pages 1-18, May.
    6. Huang, Xiaozhi & Wu, Xitong & Cao, Xin & Wu, Jifei, 2023. "The effect of medical artificial intelligence innovation locus on consumer adoption of new products," Technological Forecasting and Social Change, Elsevier, vol. 197(C).
    7. Claus Zippel & Sabine Bohnet-Joschko, 2021. "Rise of Clinical Studies in the Field of Machine Learning: A Review of Data Registered in ClinicalTrials.gov," IJERPH, MDPI, vol. 18(10), pages 1-14, May.
    8. Roman Lukyanenko & Wolfgang Maass & Veda C. Storey, 2022. "Trust in artificial intelligence: From a Foundational Trust Framework to emerging research opportunities," Electronic Markets, Springer;IIM University of St. Gallen, vol. 32(4), pages 1993-2020, December.

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