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Investigating the impacting factors for the healthcare professionals to adopt artificial intelligence-based medical diagnosis support system (AIMDSS)

Author

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  • Wenjuan Fan

    (Hefei University of Technology
    Key Laboratory of Minister of Education on Process Optimization and Intelligent Decision-making)

  • Jingnan Liu

    (Hefei University of Technology)

  • Shuwan Zhu

    (Hefei University of Technology)

  • Panos M. Pardalos

    (University of Florida)

Abstract

Compared to the booming industry of AIMDSS, the usage of AIMDSS among healthcare professionals is relatively low in the hospital. Thus, a research on the acceptance and adoption intention of AIMDSS by health professionals is imperative. In this study, an integration of Unified theory of user acceptance of technology and trust theory is proposed for exploring the adoption of AIMDSS. Besides, two groups of additional factors, related to AIMDSS (task complexity, technology characteristics, and perceived substitution crisis) and health professionals’ characteristics (propensity to trust and personal innovativeness in IT) are considered in the integrated model. The data set of proposed research model is collected through paper survey and Internet survey in China. The empirical examination demonstrates a high predictive power of this proposed model in explaining AIMDSS adoption. Finally, the theoretical contribution and practical implications of this research are discussed.

Suggested Citation

  • Wenjuan Fan & Jingnan Liu & Shuwan Zhu & Panos M. Pardalos, 2020. "Investigating the impacting factors for the healthcare professionals to adopt artificial intelligence-based medical diagnosis support system (AIMDSS)," Annals of Operations Research, Springer, vol. 294(1), pages 567-592, November.
    • Handle: RePEc:spr:annopr:v:294:y:2020:i:1:d:10.1007_s10479-018-2818-y
    DOI: 10.1007/s10479-018-2818-y
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    Cited by:

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    2. Ravi Seethamraju & Angela Hecimovic, 2023. "Adoption of artificial intelligence in auditing: An exploratory study," Australian Journal of Management, Australian School of Business, vol. 48(4), pages 780-800, November.
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    4. Vo, Vinh & Chen, Gang & Aquino, Yves Saint James & Carter, Stacy M. & Do, Quynh Nga & Woode, Maame Esi, 2023. "Multi-stakeholder preferences for the use of artificial intelligence in healthcare: A systematic review and thematic analysis," Social Science & Medicine, Elsevier, vol. 338(C).
    5. Roppelt, Julia Stefanie & Kanbach, Dominik K. & Kraus, Sascha, 2024. "Artificial intelligence in healthcare institutions: A systematic literature review on influencing factors," Technology in Society, Elsevier, vol. 76(C).
    6. Mengting Cheng & Xianmiao Li & Jicheng Xu, 2022. "Promoting Healthcare Workers’ Adoption Intention of Artificial-Intelligence-Assisted Diagnosis and Treatment: The Chain Mediation of Social Influence and Human–Computer Trust," IJERPH, MDPI, vol. 19(20), pages 1-19, October.
    7. Wu, Min & Wang, Nanxi & Yuen, Kum Fai, 2023. "Can autonomy level and anthropomorphic characteristics affect public acceptance and trust towards shared autonomous vehicles?," Technological Forecasting and Social Change, Elsevier, vol. 189(C).
    8. Wumi AJAYI & Adekoya Damola Felix & Ojarikre Oghenenerowho Princewill & Fajuyigbe Gbenga Joseph, 2024. "Software Engineering’s Key Role in AI Content Trustworthiness," International Journal of Research and Scientific Innovation, International Journal of Research and Scientific Innovation (IJRSI), vol. 11(4), pages 183-201, April.
    9. Rongbin Yang & Santoso Wibowo, 2022. "User trust in artificial intelligence: A comprehensive conceptual framework," Electronic Markets, Springer;IIM University of St. Gallen, vol. 32(4), pages 2053-2077, December.
    10. Ariel K. H. Lui & Maggie C. M. Lee & Eric W. T. Ngai, 2022. "Impact of artificial intelligence investment on firm value," Annals of Operations Research, Springer, vol. 308(1), pages 373-388, January.

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