Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2022 Jul 22:9:890809.
doi: 10.3389/fcvm.2022.890809. eCollection 2022.

Real-World and Regulatory Perspectives of Artificial Intelligence in Cardiovascular Imaging

Affiliations

Real-World and Regulatory Perspectives of Artificial Intelligence in Cardiovascular Imaging

Ernst Wellnhofer. Front Cardiovasc Med. .

Abstract

Recent progress in digital health data recording, advances in computing power, and methodological approaches that extract information from data as artificial intelligence are expected to have a disruptive impact on technology in medicine. One of the potential benefits is the ability to extract new and essential insights from the vast amount of data generated during health care delivery every day. Cardiovascular imaging is boosted by new intelligent automatic methods to manage, process, segment, and analyze petabytes of image data exceeding historical manual capacities. Algorithms that learn from data raise new challenges for regulatory bodies. Partially autonomous behavior and adaptive modifications and a lack of transparency in deriving evidence from complex data pose considerable problems. Controlling new technologies requires new controlling techniques and ongoing regulatory research. All stakeholders must participate in the quest to find a fair balance between innovation and regulation. The regulatory approach to artificial intelligence must be risk-based and resilient. A focus on unknown emerging risks demands continuous surveillance and clinical evaluation during the total product life cycle. Since learning algorithms are data-driven, high-quality data is fundamental for good machine learning practice. Mining, processing, validation, governance, and data control must account for bias, error, inappropriate use, drifts, and shifts, particularly in real-world data. Regulators worldwide are tackling twenty-first century challenges raised by "learning" medical devices. Ethical concerns and regulatory approaches are presented. The paper concludes with a discussion on the future of responsible artificial intelligence.

Keywords: innovation; machine learning (ML); regulation; safety and risk; software as a medical device (SaMD); total product life cycle (TPLC).

PubMed Disclaimer

Conflict of interest statement

The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

FIGURE 1
FIGURE 1
Comparison of access to the market in Europe as compared to the United States. MDCG Medical Device Coordination Group, CA Competent Authority, NB Notified Body, MDR Medical Device Regulation, CE certificates issued by a notified body bear a number identifying the notified body, FDA US Food and Drug Administration, CDRH FDA Center for Devices and Radiological Health.
FIGURE 2
FIGURE 2
Embedding the MLMD life-cycle in regulation—existing approaches and options.

Similar articles

Cited by

References

    1. Russell S, Russell SJ, Norvig P, Davis E. Artificial Intelligence: A Modern Approach. Upper Saddle River, NJ: Prentice Hall; (2010).
    1. Legg SH, Hutter M. A collection of definitions of intelligence. arxiv [Preprint] (2007): Available online at: https://arxiv.org/abs/0706.3639v1 (accessed December 10, 2021).
    1. Mehonic A, Kenyon AJ. Brain-inspired computing needs a master plan. Nature (2022) 604:255–60. 10.1038/s41586-021-04362-w - DOI - PubMed
    1. Ben Ali W, Pesaranghader A, Avram R, Overtchouk P, Perrin N, Laffite S, et al. Implementing machine learning in interventional cardiology: the benefits are worth the trouble. Front Cardiovasc Med. (2021) 8:711401. 10.3389/fcvm.2021.711401 - DOI - PMC - PubMed
    1. Sevakula RK, Au-Yeung WM, Singh JP, Heist EK, Isselbacher EM, Armoundas AA. State-of-the-art machine learning techniques aiming to improve patient outcomes pertaining to the cardiovascular system. J Am Heart Assoc. (2020) 9:e013924. 10.1161/JAHA.119.013924 - DOI - PMC - PubMed

LinkOut - more resources