Characterizing the clinical adoption of medical AI through U.S. insurance claims

Author:

Wu Kevin,Wu Eric,Theodorou Brandon,Liang Weixin,Mack Christina,Glass Lucas,Sun Jimeng,Zou James

Abstract

AbstractThere are now over 500 medical AI devices that are approved by the U.S. FDA. However, little is known about where and how often these devices are actually used after regulatory approval. In this paper, we systematically quantify the adoption and usage of medical AI in the U.S. 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 examine a comprehensive nationwide claims database of 16 billion CPT claims between 1/1/2015 to 6/12023 to analyze the prevalence of medical AI based on submitted claims. Our results indicate that medical AI 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, medical AI usage is moderately over-represented in higher-income zip codes and metropolitan areas. Our study sheds light on the current landscape of medical AI adoption and usage in the U.S., underscoring the need to further investigate barriers and incentives to promote equitable access and broader integration of AI technologies in healthcare.

Publisher

Cold Spring Harbor Laboratory

Reference71 articles.

1. Center for Devices & Radiological Health. Artificial intelligence and machine learning (AI/ML)-enabled medical devices. U.S. Food and Drug Administration https://www.fda.gov/medical-devices/software-medical-device-samd/artificial-intelligence-and-machine-learning-aiml-enabled-medical-devices.

2. How medical AI devices are evaluated: limitations and recommendations from an analysis of FDA approvals

3. External Validation of a Widely Implemented Proprietary Sepsis Prediction Model in Hospitalized Patients;JAMA Intern. Med,2021

4. Wu, E. , Wu, K. & Zou, J . Explaining medical AI performance disparities across sites with confounder Shapley value analysis. arXiv [cs.LG ] (2021).

5. Diagnostic Accuracy of Digital Screening Mammography With and Without Computer-Aided Detection;JAMA Intern. Med,2015

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