Document Type

Article

Publication Date

9-1-2022

Publication Title

International journal of medical informatics

Abstract

BACKGROUND: Machine learning (ML), a type of artificial intelligence (AI) technology that uses a data-driven approach for pattern recognition, has been shown to be beneficial for many tasks across healthcare. To characterize the commercial availability of AI/ML applications in the clinic, we performed a detailed analysis of AI/ML-enabled medical devices approved/cleared by the US Food and Drug Administration (FDA) by June 2021.

METHODS/MATERIALS: The publicly available approval letters by the FDA on 343 AI/ML-enabled medical devices compiled by the agency were reviewed. The characteristics of the devices and the patterns of their intended use were analyzed, and basic descriptive statistical analysis was performed on the aggregated data.

RESULTS: Most devices were reviewed by radiology (70.3%) and cardiovascular (12.0%) medical specialty panels. The growth of these devices sharply rose since the mid-2010s. Most (95.0%) devices were cleared under the 510(k) premarket notification pathway, and 69.4% were software as a medical device (SaMD). Of the 241 radiology-related devices, the most common applications were for diagnostic assistance (48.5%) and image reconstruction (14.1%). Of the 117 radiology-related devices for diagnostic assistance, 20.5% were developed for breast lesion assessment and 14.5% for cardiac function assessment on echocardiogram. Of the 41 cardiology-related devices, the most common applications were electrocardiography-based arrhythmia detection (46.3%) and hemodynamics & vital signs monitoring (26.8%).

CONCLUSION: In this study, we characterized the patterns and trends of AI/ML-enabled medical devices approved or cleared by the FDA. To our knowledge, this is the most up-to-date and comprehensive analysis of the landscape as of 2021.

Medical Subject Headings

Artificial Intelligence; Cardiology; Device Approval; Humans; Machine Learning; United States; United States Food and Drug Administration

PubMed ID

35780651

Volume

165

First Page

104828

Last Page

104828

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