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Artificial intelligence innovation in healthcare: Literature review, exploratory analysis, and future research

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  • Zahlan, Ahmed
  • Ranjan, Ravi Prakash
  • Hayes, David

Abstract

Artificial intelligence (AI) innovation in healthcare has emerged as an increasingly significant area of research. AI, digital data collection, and computer infrastructure advancements have empowered humans to address complex healthcare challenges. This study conducts a systematic literature review (SLR) of peer-reviewed journal articles at the intersection of AI, innovation, and healthcare to offer research directions for scholars and leaders in healthcare management. To achieve this, the systematic review identified and analyzed 378 published studies on AI innovation in healthcare. Evaluating these publications based on inclusion and exclusion criteria yielded 75 studies ultimately selected for comprehensive analysis. This research adds to the scope of previous investigations by aiming to 1) emphasize the most crucial AI-based healthcare applications, 2) explore challenges associated with AI integration in healthcare, and 3) examine student adoption and incorporation of AI into existing healthcare curricula. We also conducted an exploratory study of over 2700 AI-enabled healthcare startups worldwide to supplement our literature review. The SLR reveals several gaps within the research scope and proposes corresponding future research directions. These future research directions will assist researchers and enable healthcare professionals to develop legislation that accelerates the adoption of AI solutions in healthcare, ultimately enhancing public access to efficient and effective healthcare services.

Suggested Citation

  • Zahlan, Ahmed & Ranjan, Ravi Prakash & Hayes, David, 2023. "Artificial intelligence innovation in healthcare: Literature review, exploratory analysis, and future research," Technology in Society, Elsevier, vol. 74(C).
    • Handle: RePEc:eee:teinso:v:74:y:2023:i:c:s0160791x23001264
    DOI: 10.1016/j.techsoc.2023.102321
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    1. David Thesmar & David Sraer & Lisa Pinheiro & Nick Dadson & Razvan Veliche & Paul Greenberg, 2019. "Combining the Power of Artificial Intelligence with the Richness of Healthcare Claims Data: Opportunities and Challenges," PharmacoEconomics, Springer, vol. 37(6), pages 745-752, June.
    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. Galaz, Victor & Centeno, Miguel A. & Callahan, Peter W. & Causevic, Amar & Patterson, Thayer & Brass, Irina & Baum, Seth & Farber, Darryl & Fischer, Joern & Garcia, David & McPhearson, Timon & Jimenez, 2021. "Artificial intelligence, systemic risks, and sustainability," Technology in Society, Elsevier, vol. 67(C).
    4. Nikhil R. Sahni & George Stein & Rodney Zemmel & David Cutler, 2023. "The Potential Impact of Artificial Intelligence on Health Care Spending," NBER Chapters, in: The Economics of Artificial Intelligence: Health Care Challenges, pages 49-75, National Bureau of Economic Research, Inc.
    5. Ho, Manh-Tung & Le, Ngoc-Thang B. & Mantello, Peter & Ho, Manh-Toan & Ghotbi, Nader, 2023. "Understanding the acceptance of emotional artificial intelligence in Japanese healthcare system: A cross-sectional survey of clinic visitors’ attitude," Technology in Society, Elsevier, vol. 72(C).
    6. L. G. Pee & Shan L. Pan & Lili Cui, 2019. "Artificial intelligence in healthcare robots: A social informatics study of knowledge embodiment," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 70(4), pages 351-369, April.
    7. Andreas Kuckertz & Joern Block, 2021. "Reviewing systematic literature reviews: ten key questions and criteria for reviewers," Management Review Quarterly, Springer, vol. 71(3), pages 519-524, July.
    8. Liu, Xiaohui & He, Xiaoyu & Wang, Mengmeng & Shen, Huizhang, 2022. "What influences patients' continuance intention to use AI-powered service robots at hospitals? The role of individual characteristics," Technology in Society, Elsevier, vol. 70(C).
    9. Bhatia, Ridhi, 2021. "Telehealth and COVID-19: Using technology to accelerate the curve on access and quality healthcare for citizens in India," Technology in Society, Elsevier, vol. 64(C).
    10. Effy Vayena & Alessandro Blasimme & I Glenn Cohen, 2018. "Machine learning in medicine: Addressing ethical challenges," PLOS Medicine, Public Library of Science, vol. 15(11), pages 1-4, November.
    11. 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.
    12. Hajkowicz, Stefan & Sanderson, Conrad & Karimi, Sarvnaz & Bratanova, Alexandra & Naughtin, Claire, 2023. "Artificial intelligence adoption in the physical sciences, natural sciences, life sciences, social sciences and the arts and humanities: A bibliometric analysis of research publications from 1960-2021," Technology in Society, Elsevier, vol. 74(C).
    13. David Moher & Alessandro Liberati & Jennifer Tetzlaff & Douglas G Altman & The PRISMA Group, 2009. "Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA Statement," PLOS Medicine, Public Library of Science, vol. 6(7), pages 1-6, July.
    14. Davila, Antonio & Foster, George & Gupta, Mahendra, 2003. "Venture capital financing and the growth of startup firms," Journal of Business Venturing, Elsevier, vol. 18(6), pages 689-708, November.
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