Accepted for/Published in: JMIR AI
Date Submitted: Mar 14, 2023
Open Peer Review Period: Mar 14, 2023 - May 9, 2023
Date Accepted: May 7, 2023
(closed for review but you can still tweet)
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Forecasting Artificial Intelligence Trends in Healthcare: An International Patent Analysis
ABSTRACT
Background:
Background:
Artificial intelligence (AI)/machine learning (ML)-based medical devices and algorithms are rapidly changing the medical field. To provide an insight into the trends in AI and ML in healthcare, we conducted an international patent analysis.
Objective:
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Methods:
Methods:
A systematic patent analysis, focusing on AI/ML-based patents in healthcare, was performed using the Espacenet database (from 01-2012 until 07-2022). This database includes patents from the China National Intellectual Property Administration (CNIPA), European Patent Office (EPO), Japan Patent Office (JPO), Korean Intellectual Property Office (KIPO), United States Patent and Trademark Office (USPTO).
Results:
Results:
We identified 10967 patents: 7332 (66.9%) from CNIPA, 191 (1.7%) from EPO, 163 (1.5%) from JPO, 513 (4.7%) from KIPO and 2768 (25.2%) from USPTO. The number of published patents showed a yearly doubling from 2015 until 2021. Five international companies had the greatest impact on this increase: Ping An Medical and Healthcare Management with 568 (5.2%) patents, Siemens Healthineers with 273 (2.5%), IBM with 226 (2.1%), Philips Healthcare with 150 (1.4%) and Shanghai United Imaging Healthcare with 144 (1.3%).
Conclusions:
Conclusion: This international patent analysis showed a linear increase in patents published by the five largest patent offices. An open access database with interactive search options was launched for AI/ML-based patents in healthcare.
Citation
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Copyright
© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.