JOURNAL ARTICLE

Artificial Intelligence on Call: The Physician's Decision of Whether to Use AI in Clinical Practice.

  • Published In: Journal of Marketing Research (JMR), 2025, v. 62, n. 5. P. 854 1 of 3

  • Database: Business Source Ultimate 2 of 3

  • Authored By: Dai, Tinglong; Singh, Shubhranshu 3 of 3

Abstract

This article analyzes physicians’ decisions to use artificial intelligence (AI) in prescribing treatment plans, focusing on how clinical uncertainty, legal liability, and insurance reimbursement influence AI adoption. It compares two patient-protection schemes: the prevailing scheme, where AI assists but does not replace the standard of care, and an emerging scheme, where AI recommendations become the new standard of care. The study finds that physicians may overuse AI in low-uncertainty cases—using AI without following its advice—and underuse AI in high-uncertainty cases where AI could improve decisions, driven largely by liability concerns and financial incentives. Additionally, as AI precision improves, physicians might paradoxically reduce AI use for certain patients to avoid increased liability. Insurance reimbursement policies that pay physicians only when they follow AI recommendations tend to reduce inappropriate AI use. The emerging patient-protection scheme can decrease AI underuse but may increase overuse for some patients, indicating no single liability framework is universally optimal.

Additional Information

  • Source:Journal of Marketing Research (JMR). 2025/10, Vol. 62, Issue 5, p854
  • Document Type:Article
  • Subject Area:Social Sciences and Humanities
  • Publication Date:2025
  • ISSN:0022-2437
  • DOI:10.1177/00222437251332898
  • Accession Number:187649051
  • Copyright Statement:Copyright of Journal of Marketing Research (JMR) is the property of American Marketing Association and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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