Lifecycle Regulation of Artificial Intelligence– and Machine Learning–Based Software Devices in Medicine-Reference-Cited by-同舟云学术

Lifecycle Regulation of Artificial Intelligence– and Machine Learning–Based Software Devices in Medicine

Published:2019-12-17 Issue:23 Volume:322 Page:2285
ISSN:0098-7484
Container-title:JAMA
language:en
Short-container-title:JAMA

Author:

Hwang Thomas J.¹²,Kesselheim Aaron S.³,Vokinger Kerstin N.³⁴

Affiliation:

1. Harvard Medical School, Boston, Massachusetts

2. Laboratory for Technology, Markets, and Regulation, University of Zurich, Zurich, Switzerland

3. Division of Pharmacoepidemiology and Pharmacoeconomics, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts

4. Institute of Law and Laboratory for Technology, Markets, and Regulation, University of Zurich, Zurich, Switzerland

Publisher

American Medical Association (AMA)

Subject

General Medicine

Link

https://jamanetwork.com/journals/jama/articlepdf/2756194/jama_hwang_2019_vp_190145.pdf

Reference6 articles.

1. High-performance medicine.;Topol;Nat Med,2019

2. The 510(k) ancestry of a metal-on-metal hip implant.;Ardaugh;N Engl J Med,2013

3. US Food and Drug Administration. Proposed regulatory framework for modifications to artificial intelligence/machine learning (AI/ML)-based software as a medical device (SaMD). https://www.fda.gov/media/122535/download. Accessed November 5, 2019.

4. American College of Radiology. Fifth edition of the BI-RADS Atlas. https://www.acr.org/Clinical-Resources/Reporting-and-Data-Systems/Bi-Rads. Accessed November 13, 2019.

Cited by 95 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. A Justifiable Investment in AI for Healthcare: Aligning Ambition with Reality;Minds and Machines;2024-09-11

2. The status of the AI medical industry in China: A database and statistical analysis;Health Policy and Technology;2024-09

3. Mapping the regulatory landscape for artificial intelligence in health within the European Union;npj Digital Medicine;2024-08-27

4. Retrospective analysis of interpretable machine learning in predicting ICU thrombocytopenia in geriatric ICU patients;Scientific Reports;2024-07-20

5. Construction and validation the first prognostic models of progression-free survival in gastric cancer patients after gastrectomy with deficient mismatch repair: Nomogram and three machine learning models approaches;2024-07-19