Real-World and Regulatory Perspectives of Artificial Intelligence in Cardiovascular Imaging
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 a...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-07-01
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| Series: | Frontiers in Cardiovascular Medicine |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fcvm.2022.890809/full |
| _version_ | 1817971034615185408 |
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| author | Ernst Wellnhofer |
| author_facet | Ernst Wellnhofer |
| author_sort | Ernst Wellnhofer |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-13T20:41:41Z |
| format | Article |
| id | doaj.art-5297fef0917145739d6882460037f23a |
| institution | Directory Open Access Journal |
| issn | 2297-055X |
| language | English |
| last_indexed | 2024-04-13T20:41:41Z |
| publishDate | 2022-07-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Cardiovascular Medicine |
| spelling | doaj.art-5297fef0917145739d6882460037f23a2022-12-22T02:30:50ZengFrontiers Media S.A.Frontiers in Cardiovascular Medicine2297-055X2022-07-01910.3389/fcvm.2022.890809890809Real-World and Regulatory Perspectives of Artificial Intelligence in Cardiovascular ImagingErnst WellnhoferRecent 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.https://www.frontiersin.org/articles/10.3389/fcvm.2022.890809/fullmachine learning (ML)regulationinnovationsoftware as a medical device (SaMD)safety and risktotal product life cycle (TPLC) |
| spellingShingle | Ernst Wellnhofer Real-World and Regulatory Perspectives of Artificial Intelligence in Cardiovascular Imaging Frontiers in Cardiovascular Medicine machine learning (ML) regulation innovation software as a medical device (SaMD) safety and risk total product life cycle (TPLC) |
| title | Real-World and Regulatory Perspectives of Artificial Intelligence in Cardiovascular Imaging |
| title_full | Real-World and Regulatory Perspectives of Artificial Intelligence in Cardiovascular Imaging |
| title_fullStr | Real-World and Regulatory Perspectives of Artificial Intelligence in Cardiovascular Imaging |
| title_full_unstemmed | Real-World and Regulatory Perspectives of Artificial Intelligence in Cardiovascular Imaging |
| title_short | Real-World and Regulatory Perspectives of Artificial Intelligence in Cardiovascular Imaging |
| title_sort | real world and regulatory perspectives of artificial intelligence in cardiovascular imaging |
| topic | machine learning (ML) regulation innovation software as a medical device (SaMD) safety and risk total product life cycle (TPLC) |
| url | https://www.frontiersin.org/articles/10.3389/fcvm.2022.890809/full |
| work_keys_str_mv | AT ernstwellnhofer realworldandregulatoryperspectivesofartificialintelligenceincardiovascularimaging |