Literature DB >> 31818387

How the FDA Regulates AI.

H Benjamin Harvey1, Vrushab Gowda2.   

Abstract

Recent years have seen digital technologies increasingly leveraged to multiply conventional imaging modalities' diagnostic power. Artificial intelligence (AI) is most prominent among these in the radiology space, touted as the "stethoscope of the 21st century" for its potential to revolutionize diagnostic precision, provider workflow, and healthcare expenditure. Partially owing to AI's unique characteristics, and partially due to its novelty, existing regulatory paradigms are not well suited to balancing patient safety with furthering the growth of this new sector. The current review examines the historic, current, and proposed regulatory treatment of AI-empowered medical devices by the US Food and Drug Administration (FDA). An innovative framework proposed by the FDA seeks to address these issues by looking to current good manufacturing practices (cGMP) and adopting a total product lifecycle (TPLC) approach. If brought into force, this may reduce the regulatory burden incumbent on developers, while holding them to rigorous quality standards, maximizing safety, and permitting the field to mature.
Copyright © 2019 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Artificial intelligence; FDA; Medical Device; Policy; Radiology; Regulation

Year:  2020        PMID: 31818387     DOI: 10.1016/j.acra.2019.09.017

Source DB:  PubMed          Journal:  Acad Radiol        ISSN: 1076-6332            Impact factor:   3.173


  4 in total

Review 1.  Advances in and the Applicability of Machine Learning-Based Screening and Early Detection Approaches for Cancer: A Primer.

Authors:  Leo Benning; Andreas Peintner; Lukas Peintner
Journal:  Cancers (Basel)       Date:  2022-01-26       Impact factor: 6.639

2.  Tempering Expectations on the Medical Artificial Intelligence Revolution: The Medical Trainee Viewpoint.

Authors:  Zoe Hu; Ricky Hu; Olivia Yau; Minnie Teng; Patrick Wang; Grace Hu; Rohit Singla
Journal:  JMIR Med Inform       Date:  2022-08-15

3.  Reporting Guidelines for Artificial Intelligence in Medical Research.

Authors:  J Peter Campbell; Aaron Y Lee; Michael Abràmoff; Pearse A Keane; Daniel S W Ting; Flora Lum; Michael F Chiang
Journal:  Ophthalmology       Date:  2020-09-10       Impact factor: 12.079

Review 4.  Algorithmic prognostication in critical care: a promising but unproven technology for supporting difficult decisions.

Authors:  Gary E Weissman; Vincent X Liu
Journal:  Curr Opin Crit Care       Date:  2021-10-01       Impact factor: 3.359

  4 in total

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