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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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    1. Pham, Phuoc & Zhang, Huilan & Gao, Wenlian & Zhu, Xiaowei, 2024. "Determinants and performance outcomes of artificial intelligence adoption: Evidence from U.S. Hospitals," Journal of Business Research, Elsevier, vol. 172(C).
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    3. Hajiheydari, Nastaran & Delgosha, Mohammad Soltani & Saheb, Tahereh, 2025. "AI in medical diagnosis: A contextualised study of patient motivations and concerns," Social Science & Medicine, Elsevier, vol. 371(C).
    4. Heising, Luca M. & Ou, Carol X. J. & Verhaegen, Frank & Wolfs, Cecile J. A. & Hoebers, Frank & Jacobs, Maria J. G., 2024. "Trust in Artificial Intelligence in radiotherapy : A survey," Other publications TiSEM 870b35f8-ded2-401e-b228-4, Tilburg University, School of Economics and Management.
    5. W. Shabeena Shah & Zakaria Elkhwesky & K. Mohamed Jasim & Esraa Fayez Youssif Elkhwesky & Fady Fayez Youssif Elkhwesky, 2024. "Artificial intelligence in healthcare services: past, present and future research directions," Review of Managerial Science, Springer, vol. 18(3), pages 941-963, March.
    6. Ana Rita Pedro & Michelle B Dias & Liliana Laranjo & Ana Soraia Cunha & João V Cordeiro, 2023. "Artificial intelligence in medicine: A comprehensive survey of medical doctor’s perspectives in Portugal," PLOS ONE, Public Library of Science, vol. 18(9), pages 1-24, September.
    7. 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.
    8. 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).
    9. Omrani, Nessrine & Rivieccio, Giorgia & Fiore, Ugo & Schiavone, Francesco & Agreda, Sergio Garcia, 2022. "To trust or not to trust? An assessment of trust in AI-based systems: Concerns, ethics and contexts," Technological Forecasting and Social Change, Elsevier, vol. 181(C).
    10. 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.
    11. 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).
    12. 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).
    13. 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).
    14. 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.
    15. 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.
    16. Marc Robert & Remi Goff & Sophie Mignon & Philippe Giuliani, 2025. "Decoding the significant role of social context in SMEs’ implementation of management innovation during the digital revolution," Annals of Operations Research, Springer, vol. 348(3), pages 1953-1987, May.
    17. Hikaru Goto & H. M. Belal & Kunio Shirahada, 2025. "Value co-destruction causing customers to stop service usage: a topic modelling analysis of dental service complaint data," Annals of Operations Research, Springer, vol. 348(3), pages 1691-1711, May.
    18. 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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