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

作者信息

Benjamens Stan, Dhunnoo Pranavsingh, Meskó Bertalan

机构信息

Department of Surgery, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands.

Medical Imaging Center, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands.

出版信息

NPJ Digit Med. 2020 Sep 11;3:118. doi: 10.1038/s41746-020-00324-0. eCollection 2020.


DOI:10.1038/s41746-020-00324-0
PMID:32984550
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7486909/
Abstract

At the beginning of the artificial intelligence (AI)/machine learning (ML) era, the expectations are high, and experts foresee that AI/ML shows potential for diagnosing, managing and treating a wide variety of medical conditions. However, the obstacles for implementation of AI/ML in daily clinical practice are numerous, especially regarding the regulation of these technologies. Therefore, we provide an insight into the currently available AI/ML-based medical devices and algorithms that have been approved by the US Food & Drugs Administration (FDA). We aimed to raise awareness of the importance of regulatory bodies, clearly stating whether a medical device is AI/ML based or not. Cross-checking and validating all approvals, we identified 64 AI/ML based, FDA approved medical devices and algorithms. Out of those, only 29 (45%) mentioned any AI/ML-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 have launched the first comprehensive and open access database of strictly AI/ML-based medical technologies that have been approved by the FDA. The database will be constantly updated.

摘要
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a03a/7486909/6cef3fb45ed9/41746_2020_324_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a03a/7486909/69aae7022356/41746_2020_324_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a03a/7486909/6cef3fb45ed9/41746_2020_324_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a03a/7486909/69aae7022356/41746_2020_324_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a03a/7486909/6cef3fb45ed9/41746_2020_324_Fig2_HTML.jpg

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[1]
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Int J Methods Psychiatr Res. 2020-6

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Multimodal wrist-worn devices for seizure detection and advancing research: Focus on the Empatica wristbands.

Epilepsy Res. 2019-7

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Nat Med. 2019-1-7

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