IDEAS home Printed from https://ideas.repec.org/a/gam/jbusin/v5y2025i4p44-d1756848.html

Factors Influencing the Adoption of Artificial Intelligence in Healthcare: A Study on the Role of Knowledge and Benefits in Clinical and Managerial Decision-Making

Author

Listed:
  • Renato Lopes da Costa

    (Departamento de Marketing, Operações e Gestão Geral, ISCTE-Instituto Universitário de Lisboa, Av. das Forças Armadas, 1649-026 Lisboa, Portugal)

  • Mário Pereira

    (Departamento de Marketing, Operações e Gestão Geral, ISCTE-Instituto Universitário de Lisboa, Av. das Forças Armadas, 1649-026 Lisboa, Portugal)

  • António Angelo Pereira

    (Departamento de Marketing, Operações e Gestão Geral, ISCTE-Instituto Universitário de Lisboa, Av. das Forças Armadas, 1649-026 Lisboa, Portugal)

  • João Canas

    (Departamento de Marketing, Operações e Gestão Geral, ISCTE-Instituto Universitário de Lisboa, Av. das Forças Armadas, 1649-026 Lisboa, Portugal)

  • Ricardo Correia

    (Departamento de Gestão e Economia, Universidade da Madeira, Praça do Município, São Martinho, 9000-072 Funchal, Portugal)

  • Cláudio Dimande

    (ISCTEM—Instituto Superior de Ciências e Tecnologia de Moçambique, Rua 1, 194-332, Bairro Central, Maputo 1100, Mozambique)

Abstract

In recent years, Artificial Intelligence (AI) health projects have attracted significant investment, driven by a 50% annual increase in stored data. This growth has led to the development of AI tools that assist health professionals and managers in decision-making within clinical practice and healthcare management. This research seeks to identify key factors influencing the adoption of AI systems by health professionals and managerial staff, drawing on a sample of 100 respondents, of whom 61 are healthcare practitioners and 39 occupy management positions within the health sector. An extensive literature review and a statistical analysis using SmartPLS 3 were conducted. The findings suggest that the level of knowledge and perceived benefits positively impact the intention to implement AI tools. Interestingly, challenges during AI adoption do not hinder professionals. The study highlights the importance of integrating AI into healthcare professionals’ careers and emphasises the need for measures to enhance their understanding of AI algorithms. Increased knowledge fosters greater willingness to adopt AI systems, promoting improved decision-making in healthcare.

Suggested Citation

  • Renato Lopes da Costa & Mário Pereira & António Angelo Pereira & João Canas & Ricardo Correia & Cláudio Dimande, 2025. "Factors Influencing the Adoption of Artificial Intelligence in Healthcare: A Study on the Role of Knowledge and Benefits in Clinical and Managerial Decision-Making," Businesses, MDPI, vol. 5(4), pages 1-15, September.
    • Handle: RePEc:gam:jbusin:v:5:y:2025:i:4:p:44-:d:1756848
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2673-7116/5/4/44/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2673-7116/5/4/44/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Xiangbin Liu & Liping Song & Shuai Liu & Yudong Zhang, 2021. "A Review of Deep-Learning-Based Medical Image Segmentation Methods," Sustainability, MDPI, vol. 13(3), pages 1-29, January.
    2. Michael Fairley & David Scheinker & Margaret L. Brandeau, 2019. "Improving the efficiency of the operating room environment with an optimization and machine learning model," Health Care Management Science, Springer, vol. 22(4), pages 756-767, December.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yuan Shi & Saied Mahdian & Jose Blanchet & Peter Glynn & Andrew Y. Shin & David Scheinker, 2023. "Surgical scheduling via optimization and machine learning with long-tailed data," Health Care Management Science, Springer, vol. 26(4), pages 692-718, December.
    2. Azar, Macarena & Carrasco, Rodrigo A. & Mondschein, Susana, 2022. "Dealing with uncertain surgery times in operating room scheduling," European Journal of Operational Research, Elsevier, vol. 299(1), pages 377-394.
    3. Jongkyung Shin & Donggi Augustine Lee & Juram Kim & Chiehyeon Lim & Byung-Kwan Choi, 2024. "Dissatisfaction-considered waiting time prediction for outpatients with interpretable machine learning," Health Care Management Science, Springer, vol. 27(3), pages 370-390, September.
    4. Manoj Kumar & Kranti Kumar, 2026. "Hytranet: a deep learning model for traffic congestion prediction in road networks," Journal of Combinatorial Optimization, Springer, vol. 51(1), pages 1-22, January.
    5. Hassan, Sharmarke & Dhimish, Mahmoud, 2023. "Enhancing solar photovoltaic modules quality assurance through convolutional neural network-aided automated defect detection," Renewable Energy, Elsevier, vol. 219(P1).
    6. Xiaoyue Gong & Jian-Jun Wang, 2026. "An environmentally friendly elective patient scheduling under the predict-then-optimize framework," Annals of Operations Research, Springer, vol. 359(2), pages 2235-2275, April.
    7. Navin Ranjan & Sovit Bhandari & Pervez Khan & Youn-Sik Hong & Hoon Kim, 2021. "Large-Scale Road Network Congestion Pattern Analysis and Prediction Using Deep Convolutional Autoencoder," Sustainability, MDPI, vol. 13(9), pages 1-26, May.
    8. Michael R. Johnson & Hiten Naik & Wei Siang Chan & Jesse Greiner & Matt Michaleski & Dong Liu & Bruno Silvestre & Ian P. McCarthy, 2023. "Forecasting ward-level bed requirements to aid pandemic resource planning: Lessons learned and future directions," Health Care Management Science, Springer, vol. 26(3), pages 477-500, September.
    9. Masoud Eshghali & Devika Kannan & Navid Salmanzadeh-Meydani & Amir Mohammad Esmaieeli Sikaroudi, 2024. "Machine learning based integrated scheduling and rescheduling for elective and emergency patients in the operating theatre," Annals of Operations Research, Springer, vol. 332(1), pages 989-1012, January.
    10. Md Al Amin & Roberto Baldacci & Vahid Kayvanfar, 2025. "A comprehensive review on operating room scheduling and optimization," Operational Research, Springer, vol. 25(1), pages 1-30, March.
    11. David Scheinker & Margaret L. Brandeau, 2020. "Implementing Analytics Projects in a Hospital: Successes, Failures, and Opportunities," Interfaces, INFORMS, vol. 50(3), pages 176-189, May.
    12. Jiang, Man & Yang, Siluo & Gao, Qiang, 2024. "Multidimensional indicators to identify emerging technologies: Perspective of technological knowledge flow," Journal of Informetrics, Elsevier, vol. 18(1).
    13. Aleida Braaksma & Martin S. Copenhaver & Ana C. Zenteno & Elizabeth Ugarph & Retsef Levi & Bethany J. Daily & Benjamin Orcutt & Kathryn M. Turcotte & Peter F. Dunn, 2023. "Evaluation and implementation of a Just-In-Time bed-assignment strategy to reduce wait times for surgical inpatients," Health Care Management Science, Springer, vol. 26(3), pages 501-515, September.

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jbusin:v:5:y:2025:i:4:p:44-:d:1756848. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager The email address of this maintainer does not seem to be valid anymore. Please ask MDPI Indexing Manager to update the entry or send us the correct address (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.