Characterizing the clinical adoption of medical AI devices through U.S. insurance claims
Open accessAbstract
There are now over 500 medical artificial intelligence (AI) devices that are approved by the U.S. Food and Drug Administration. However, little is known about where and how often these devices are actually used after regulatory approval. In this article, we systematically quantify the adoption and usage of medical AI devices in the United States by tracking Current Procedural Terminology (CPT) codes explicitly created for medical AI. CPT codes are widely used for documenting billing and payment for medical procedures, providing a measure of device utilization across different clinical settings. We examined a comprehensive nationwide claims database of 11 billion CPT claims between January 1, 2018, and June 1, 2023 to analyze the prevalence of medical AI devices based on submitted claims. Our results indicate that medical AI device adoption is still nascent, with most usage driven by a handful of leading devices. For example, only AI devices used for assessing coronary artery disease and for diagnosing diabetic retinopathy have accumulated more than 10,000 CPT claims. Furthermore, we found that zip codes that had a higher income level, were metropolitan, and had academic medical centers were much more likely to have medical AI usage. Our study sheds light on the current landscape of medical AI device adoption and usage in the United States, underscoring the need to further investigate barriers and incentives to promote equitable access and broader integration of AI technologies in health care.
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Utilization of autonomous AI for DR testing has been scaling rapidly.
“and as a result utilization has been scaling rapidly26,27.”
From Autonomous AI-assisted diabetic retinopathy screening at primary care is associated with increased presentation to eye care by at risk patients (2026) · cites this paper as [26]
Lack of reimbursement makes it challenging to assess the true extent of clinical integration, because traditional methods such as insurance claims data cannot be used except for a small number of applications.
“This lack of reimbursement also makes it challenging to assess the true extent of clinical integration, as traditional methods for measuring medical device use such as insurance claims data cannot be used except for a small number of applications (211).”
From Artificial Intelligence in Oncology: Current Landscape, Challenges, and Future Directions (2024) · cites this paper as [211]
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- Authors
- Wu, K., Wu, E., Theodorou, B., Liang, W., Mack, C., Glass, L., Sun, J., & Zou, J.
- Affiliations
- Stanford University (US)
- University of Illinois Urbana-Champaign (US)
- IQVIA (United States) (US)
- ORCID
- Venue
- NEJM AI
- Publisher
- Massachusetts Medical Society
- Published
- 2024-01
- Volume
- 1
- Issue
- 1
- Pages
- AIoa2300030-AIoa2300030
- ISSN
- 2836-9386 (ISSN-L)
- OpenAlex
- W4388525108
- Cited by
- 103
- References
- 45
- Citations by year
- 2026: 30 · 2025: 47 · 2024: 25 · 2023: 1
- FWCI
- 4.11
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- NEJM AI (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Healthcare Policy and Management
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Technology and Human Factors in Education and Health
- Keywords
- clinical adoption, CPT codes, insurance claims, device utilization, coronary artery disease, diabetic retinopathy, healthcare access equity, academic medical centers
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