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Previously submitted to: Journal of Medical Internet Research (no longer under consideration since Mar 30, 2020)

Date Submitted: Mar 25, 2020
Open Peer Review Period: Mar 25, 2020 - Mar 30, 2020
(closed for review but you can still tweet)

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The state of artificial intelligence-based FDA-approved medical devices and algorithms: An online database

  • Bertalan Mesko; 
  • Stan Benjamens; 
  • Pranavsingh Dhunnoo; 



At the beginning of the artificial intelligence (A.I.) era, the expectations are high, and experts foresee that A.I. shows potential for diagnosing, managing and treating a wide variety of medical conditions. However, the obstacles for implementation of A.I. in daily clinical practice are numerous, especially regarding the regulation of these technologies.


Therefore, we provide an insight into the currently available A.I.-based medical devices and algorithms that have been approved by the U.S. Food & Drugs Administration (FDA). We aimed to raise awareness about the importance of regulatory bodies, clearly stating whether a medical device is A.I.-based or not.


Cross-checking and validating all approvals, we identified 64 A.I.-based, FDA approved medical devices and algorithms. Out of those, only 29 (45%) mentioned any A.I.-related expressions in the official FDA announcement.


The majority (85.9%) was approved by the FDA with a 510(k) clearance, while 8 (12.5%) received de novo pathway clearance and one (1.6%) premarket approval (PMA) clearance. Most of these technologies, notably 30 (46.9%), 16 (25.0%) and 10 (15.6%) were developed for the fields of Radiology, Cardiology and Internal Medicine / General Practice respectively.


We launched the first comprehensive and open access database of strictly A.I.-based medical technologies that have been approved by the FDA. The database will be constantly updated. 


Please cite as:

Mesko B, Benjamens S, Dhunnoo P

The state of artificial intelligence-based FDA-approved medical devices and algorithms: An online database

JMIR Preprints. 25/03/2020:18882

DOI: 10.2196/preprints.18882


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