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Technological paradoxes and artificial intelligence implementation in healthcare. An application of paradox theory

Author

Listed:
  • Nidhi Singh

    (Jaipuria Institute of Management [Noida])

  • Monika Jain

    (BIMTECH - Birla Institute of Management Technology)

  • Muhammad Mustafa Kamal

    (Coventry University)

  • Rahul Bodhi

    (UPES - University of Petroleum and Energy Studies [Dehradun])

  • Bhumika Gupta

    (LITEM - Laboratoire en Innovation, Technologies, Economie et Management (EA 7363) - UEVE - Université d'Évry-Val-d'Essonne - Université Paris-Saclay - IMT-BS - Institut Mines-Télécom Business School - IMT - Institut Mines-Télécom [Paris], IMT-BS - MMS - Département Management, Marketing et Stratégie - TEM - Télécom Ecole de Management - IMT - Institut Mines-Télécom [Paris] - IMT-BS - Institut Mines-Télécom Business School - IMT - Institut Mines-Télécom [Paris])

Abstract

AI is transforming healthcare system with many innovations in diagnosis, drug research and advancement in medical treatments. But there are several concerns and dilemmas related to data misuse, AI efficiency for critical diagnostic services, users' resistance, investment costs, funding issues, and so on that have been raised by many previous studies on the effective integration of AI in clinical settings. Using paradox theory in the organisational settings, the present study discusses several technological paradoxes associated with the adoption of AI in healthcare. In this regard, the study examines the views of diverse medical practitioners about using AI services for several medical needs. The study analyses the efficacy and limitations of AI services which develop several ethical dilemmas in the mind of medical practitioners and also suggest a few strategies for the adoption. Using grounded theory approach, the study collected views of 62 medical practitioners on these dimensions. The primary drivers to the adoption identified in the present study are: ease of use, automation efficacy, diagnostic accuracy, and cost efficiency. A lack of training and education, cultural and religious considerations, privacy issues and work insecurity are some of the concerns highlighted by the medical staffs. The study inferred a few paradoxes or ethical dilemmas of practitioners which need attentions. The study contributes to the existing literature on paradox theory and AI, and identifies a few under-discussed areas, drivers, and barriers of AI services are highlighted in the paper, which may lead to ethical concerns and steer AI adoption in healthcare.

Suggested Citation

  • Nidhi Singh & Monika Jain & Muhammad Mustafa Kamal & Rahul Bodhi & Bhumika Gupta, 2024. "Technological paradoxes and artificial intelligence implementation in healthcare. An application of paradox theory," Post-Print hal-04300238, HAL.
    • Handle: RePEc:hal:journl:hal-04300238
    DOI: 10.1016/j.techfore.2023.122967
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    Citations

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    Cited by:

    1. Moriuchi, Emi, 2025. "Deploying artificial intelligence for healthcare: An empirical study on the patients' usage of health care service and AI's anthropomorphic structure form," Technological Forecasting and Social Change, Elsevier, vol. 213(C).
    2. Singh, Kuldeep & Chatterjee, Sheshadri & Mariani, Marcello, 2024. "Applications of generative AI and future organizational performance: The mediating role of explorative and exploitative innovation and the moderating role of ethical dilemmas and environmental dynamis," Technovation, Elsevier, vol. 133(C).
    3. Irgang, Luís & Sestino, Andrea & Barth, Henrik & Holmén, Magnus, 2025. "Healthcare workers' adoption of and satisfaction with artificial intelligence: The counterintuitive role of paradoxical tensions and paradoxical mindset," Technological Forecasting and Social Change, Elsevier, vol. 212(C).
    4. Zhu, Xingzhen & Li, Li & Li, Jing, 2025. "To offer or not to offer? Bullet screen strategies for competing video platforms with vertical differentiation," Journal of Retailing and Consumer Services, Elsevier, vol. 82(C).
    5. Huang, Meiying & Li, Quan & Li, Bowen, 2025. "From algorithms to green growth: Can artificial intelligence drive enterprise energy transformation?," Economic Analysis and Policy, Elsevier, vol. 85(C), pages 1846-1866.
    6. Kahouli, Bassem & Omri, Anis & Afi, Hatem, 2024. "Technological innovations and health performance: Effects and transmission channels," Technological Forecasting and Social Change, Elsevier, vol. 204(C).
    7. Issa, Helmi & Jaber, Jad & Lakkis, Hussein, 2024. "Navigating AI unpredictability: Exploring technostress in AI-powered healthcare systems," Technological Forecasting and Social Change, Elsevier, vol. 202(C).
    8. Zhao, Xiongfei & Li, Shuangjie, 2025. "Artificial intelligence and public environmental concern: Impacts on green innovation transformation in energy-intensive enterprises," Energy Policy, Elsevier, vol. 198(C).
    9. Meyer, Lea Mareen & Stead, Susan & Salge, Torsten Oliver & Antons, David, 2024. "Artificial intelligence in acute care: A systematic review, conceptual synthesis, and research agenda," Technological Forecasting and Social Change, Elsevier, vol. 206(C).

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