Concepts in U.S. Food and Drug Administration Regulation of Artificial Intelligence for Medical Imaging

Kohli, Ajay; Mahajan, Vidur; Seals, Kevin; Kohli, Ajit; Jha, Saurabh

doi:10.2214/ajr.18.20410

Cited by 26 publications

(12 citation statements)

References 2 publications

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“…To be lawfully put on the US market, a medical device must be reviewed by the FDA [36,37], using either the De Novo pathway, for innovative medical devices that have no equivalent (FDA approval) [38]; or the 510(k) pathway, for medical devices that have an equivalent predicate already on the US market (FDA clearance) [39].…”

Section: Does the Ai Application Comply With The Local Medical Devicementioning

confidence: 99%

To buy or not to buy—evaluating commercial AI solutions in radiology (the ECLAIR guidelines)

Omoumi

Ducarouge²,

Tournier³

et al. 2021

Eur Radiol

120

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Artificial intelligence (AI) has made impressive progress over the past few years, including many applications in medical imaging. Numerous commercial solutions based on AI techniques are now available for sale, forcing radiology practices to learn how to properly assess these tools. While several guidelines describing good practices for conducting and reporting AI-based research in medicine and radiology have been published, fewer efforts have focused on recommendations addressing the key questions to consider when critically assessing AI solutions before purchase. Commercial AI solutions are typically complicated software products, for the evaluation of which many factors are to be considered. In this work, authors from academia and industry have joined efforts to propose a practical framework that will help stakeholders evaluate commercial AI solutions in radiology (the ECLAIR guidelines) and reach an informed decision. Topics to consider in the evaluation include the relevance of the solution from the point of view of each stakeholder, issues regarding performance and validation, usability and integration, regulatory and legal aspects, and financial and support services. Key Points • Numerous commercial solutions based on artificial intelligence techniques are now available for sale, and radiology practices have to learn how to properly assess these tools. • We propose a framework focusing on practical points to consider when assessing an AI solution in medical imaging, allowing all stakeholders to conduct relevant discussions with manufacturers and reach an informed decision as to whether to purchase an AI commercial solution for imaging applications. • Topics to consider in the evaluation include the relevance of the solution from the point of view of each stakeholder, issues regarding performance and validation, usability and integration, regulatory and legal aspects, and financial and support services.

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Section: Does the Ai Application Comply With The Local Medical Devicementioning

confidence: 99%

To buy or not to buy—evaluating commercial AI solutions in radiology (the ECLAIR guidelines)

Omoumi

Ducarouge²,

Tournier³

et al. 2021

Eur Radiol

120

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“…Not all the variants are associated with genetic variants only deleterious one are involved in Mendelian diseases, cancers etc. [18]. Identification of these deleterious variants at a time is a much laborious and time consuming procedure due to its complexity.…”

Section: ) Computational Methods For the Classifications Of Variantsmentioning

confidence: 99%

Role of Artificial Intelligence and Machine Learning in Bioinformatics: Drug Discovery and Drug Repurposing

Quazi¹

2021

Preprint

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Artificial intelligence AI or machine learning has proven to be a potential activity in the health and biomedical sciences. Previous research it has found that AI can learn new data and transform it into the useful knowledge. In the field of pharmacology, the aim is to design more efficient and novel vaccines using this method which are also cost effective. The underlying fact is to predict the molecular mechanism and structure for increased likelihood of developing new drugs. Clinical, electronic and high resolution imaging datasets can be used as inputs to aid the drug development niche. Moreover, the use of comprehensive target activity has been performed for repurposing a drug molecule by extending target profiles of drugs which also include off targets with therapeutic potential providing a new indication.

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“…The ITU/WHO Focus Group on artificial intelligence for health (FG-AI4H) cooperates with the World Health Organization (WHO) to establish standards to assess AI applications in the health sector. 56 artificial intelligence 59 has a scope on trustworthy AI. Requirements to manage AI-specific security, safety, and privacy risks and general requirements for management of weaknesses, threats, and other challenges are proposed based on an analysis of other standards, particularly general risk-management (ISO 31000) and the ISO/IEC 250xx standards series.…”

Section: Pertinent Standardsmentioning

confidence: 99%

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

Wellnhofer

2022

Front. Cardiovasc. Med.

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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.

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Concepts in U.S. Food and Drug Administration Regulation of Artificial Intelligence for Medical Imaging

Cited by 26 publications

References 2 publications

To buy or not to buy—evaluating commercial AI solutions in radiology (the ECLAIR guidelines)

To buy or not to buy—evaluating commercial AI solutions in radiology (the ECLAIR guidelines)

Role of Artificial Intelligence and Machine Learning in Bioinformatics: Drug Discovery and Drug Repurposing

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

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