IDEAS home Printed from https://ideas.repec.org/h/nbr/nberch/14758.html
   My bibliography  Save this book chapter

Introduction to "The Economics of Artificial Intelligence: Health Care Challenges"

In: The Economics of Artificial Intelligence: Health Care Challenges

Author

Listed:
  • Ajay Agrawal
  • Joshua Gans
  • Avi Goldfarb
  • Catherine Tucker

Abstract

No abstract is available for this item.

Suggested Citation

  • Ajay Agrawal & Joshua Gans & Avi Goldfarb & Catherine Tucker, 2023. "Introduction to "The Economics of Artificial Intelligence: Health Care Challenges"," NBER Chapters, in: The Economics of Artificial Intelligence: Health Care Challenges, pages 1-7, National Bureau of Economic Research, Inc.
  • Handle: RePEc:nbr:nberch:14758
    as

    Download full text from publisher

    File URL: http://www.nber.org/chapters/c14758.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ajay Agrawal & Joshua Gans & Avi Goldfarb, 2019. "The Economics of Artificial Intelligence: An Agenda," NBER Books, National Bureau of Economic Research, Inc, number agra-1, March.
    2. Avi Goldfarb & Bledi Taska & Florenta Teodoridis, 2020. "Artificial Intelligence in Health Care? Evidence from Online Job Postings," AEA Papers and Proceedings, American Economic Association, vol. 110, pages 400-404, May.
    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. Nicholas Bloom & Tarek Alexander Hassan & Aakash Kalyani & Josh Lerner & Ahmed Tahoun, 2021. "The diffusion of disruptive technologies," CEP Discussion Papers dp1798, Centre for Economic Performance, LSE.
    2. Colin Wessendorf & Alexander Kopka & Dirk Fornahl, 2021. "The impact of the six European Key Enabling Technologies (KETs) on regional knowledge creation," Papers in Evolutionary Economic Geography (PEEG) 2127, Utrecht University, Department of Human Geography and Spatial Planning, Group Economic Geography, revised Sep 2021.
    3. Oliver Falck & Johannes Koenen, 2020. "Rohstoff „Daten“: Volkswirtschaflicher Nutzen von Datenbereitstellung – eine Bestandsaufnahme," ifo Forschungsberichte, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, number 113.
    4. Christian Rammer & Gastón P Fernández & Dirk Czarnitzki, 2021. "Artificial Intelligence and Industrial Innovation: Evidence from Firm-Level Data," Working Papers of Department of Economics, Leuven 674605, KU Leuven, Faculty of Economics and Business (FEB), Department of Economics, Leuven.
    5. Byron Botha & Rulof Burger & Kevin Kotzé & Neil Rankin & Daan Steenkamp, 2023. "Big data forecasting of South African inflation," Empirical Economics, Springer, vol. 65(1), pages 149-188, July.
    6. Philippe Goulet Coulombe & Maxime Leroux & Dalibor Stevanovic & Stéphane Surprenant, 2022. "How is machine learning useful for macroeconomic forecasting?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(5), pages 920-964, August.
    7. Czarnitzki, Dirk & Fernández, Gastón P. & Rammer, Christian, 2023. "Artificial intelligence and firm-level productivity," Journal of Economic Behavior & Organization, Elsevier, vol. 211(C), pages 188-205.
    8. DUERNECKER Georg & SANCHEZ MARTINEZ Miguel, 2021. "Structural change and productivity growth in the European Union: Past, present and future," JRC Working Papers on Territorial Modelling and Analysis 2021-09, Joint Research Centre.
    9. Ekaterina Prytkova, 2021. "ICT's Wide Web: a System-Level Analysis of ICT's Industrial Diffusion with Algorithmic Links," Jena Economics Research Papers 2021-005, Friedrich-Schiller-University Jena.
    10. Venkat Ram Reddy Ganuthula, 2025. "AI-enabled individual entrepreneurship theory: redefining scale, capability, and sustainability in the digital age," Journal of Innovation and Entrepreneurship, Springer, vol. 14(1), pages 1-21, December.
    11. Combes, Pierre-Philippe & Gobillon, Laurent & Zylberberg, Yanos, 2022. "Urban economics in a historical perspective: Recovering data with machine learning," Regional Science and Urban Economics, Elsevier, vol. 94(C).
    12. Rama K. Malladi, 2024. "Benchmark Analysis of Machine Learning Methods to Forecast the U.S. Annual Inflation Rate During a High-Decile Inflation Period," Computational Economics, Springer;Society for Computational Economics, vol. 64(1), pages 335-375, July.
    13. Andrew Green & Lucas Lamby, 2025. "The Characteristics of the Artificial Intelligence Workforce across OECD Countries," The Indian Journal of Labour Economics, Springer;The Indian Society of Labour Economics (ISLE), vol. 68(2), pages 541-568, June.
    14. Mohammad Hossein Azin & Hessam Zandhessami, 2025. "Strategic Alignment Patterns in National AI Policies," Papers 2507.05400, arXiv.org, revised Jul 2025.
    15. Kristina McElheran & J. Frank Li & Erik Brynjolfsson & Zachary Kroff & Emin Dinlersoz & Lucia Foster & Nikolas Zolas, 2024. "AI adoption in America: Who, what, and where," Journal of Economics & Management Strategy, Wiley Blackwell, vol. 33(2), pages 375-415, March.
    16. Mert Demirer & Diego Jimenez-Hernandez & Dean Li & Sida Peng, 2024. "Data, Privacy Laws and Firm Production: Evidence from the GDPR," Working Paper Series WP 2024-02, Federal Reserve Bank of Chicago.
    17. Carlo Drago & Alberto Costantiello & Marco Savorgnan & Angelo Leogrande, 2025. "Macroeconomic and Labor Market Drivers of AI Adoption in Europe: A Machine Learning and Panel Data Approach," Economies, MDPI, vol. 13(8), pages 1-62, August.
    18. E. Mark Curtis & Ioana Marinescu, 2023. "Green Energy Jobs in the United States: What Are They, and Where Are They?," Environmental and Energy Policy and the Economy, University of Chicago Press, vol. 4(1), pages 202-237.
    19. Wang, Li & Wu, Yuhan & Huang, Zeyu & Wang, Yanan, 2024. "Big data application and corporate investment decisions: Evidence from A-share listed companies in China," International Review of Financial Analysis, Elsevier, vol. 94(C).
    20. Vasiliki Koniakou, 2023. "From the “rush to ethics” to the “race for governance” in Artificial Intelligence," Information Systems Frontiers, Springer, vol. 25(1), pages 71-102, February.

    More about this item

    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:nbr:nberch:14758. 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: the person in charge (email available below). General contact details of provider: https://edirc.repec.org/data/nberrus.html .

    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.