How the FDA Regulates AI

Harvey, H. Benjamin; Gowda, Vrushab

doi:10.1016/j.acra.2019.09.017

Cited by 68 publications

(35 citation statements)

References 8 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

112

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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

112

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“…With the increased adoption of AI/ML in this domain, there is a growing recognition and demand to regulate AI/ML in healthcare to avoid potential harm and unfair bias against vulnerable populations. The regulatory bodies like the FDA [12], European Union's GDPR, China's New Generation AI Governance Expert Committee [15] etc. have either promulgated or put forward regulatory frameworks for responsible AI [3][6][9].…”

Section: Extended Abstractmentioning

confidence: 99%

“…In this tutorial we offer a guide to help navigate through the complex regulations and explain the various constituent practical elements of a responsible AI system in healthcare in the light of proposed regulations. Additionally, we breakdown and emphasize that the recommendations from regulatory bodies like FDA [12] or the EU are necessary but not sufficient elements of creating a responsible AI system.…”

Section: Extended Abstractmentioning

confidence: 99%

Software as a Medical Device

Ahmad

Overman

Allen

et al. 2021

Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery &Amp; Data Mining

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With the increased adoption of AI in healthcare, there is a growing recognition and demand to regulate AI in healthcare to avoid potential harm and unfair bias against vulnerable populations. Around a hundred governmental bodies and commissions as well as leaders in the tech sector have proposed principles to create responsible AI systems. However, most of these proposals are short on specifics which has led to charges of ethics washing. In this tutorial we offer a guide to help navigate through complex governmental regulations and explain the various constituent practical elements of a responsible AI system in healthcare in the light of proposed regulations. Additionally, we breakdown and emphasize that the recommendations from regulatory bodies like FDA or the EU are necessary but not sufficient elements of creating a responsible AI system. We elucidate how regulations and guidelines often focus on epistemic concerns to the detriment of practical concerns e.g., requirement for fairness without explicating what fairness constitutes for a use case. FDA's Software as a medical device document and EU's GDPR among other AI governance documents talk about the need for implementing sufficiently good machine learning practices. In this tutorial we elucidate what that would mean from a practical perspective for real world use cases in healthcare throughout the machine learning cycle i.e., Data Management, Data Specification, Feature Engineering, Model Evaluation, Model Specification, Model Explainability, Model Fairness, Reproducibility, checks for data leakage and model leakage. We note that conceptualizing responsible AI as a process rather than an end goal accords well with how AI systems are used in practice. We also discuss how a domain centric stakeholder perspective translates into balancing requirements for multiple competing optimization criteria.

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“…12,16 Looking at the big picture, the standardization of reporting guidelines of AI will help (1) to ensure validity, improve replicability, and maximize the usefulness of clinical research; (2) to streamline and guide the approval pathway by the regulatory committee (e.g., United States Food and Drug Administration, European Conformité Européenne [CE], etc.) 17 ; and (3) to improve patient safety, outcomes, andd we hopedexperience. 13 Although the CONSORT-AI and SPIRIT-AI extensions are designed for those writing (and reviewing) manuscripts involving AI algorithms, we believe they are equally and perhaps more importantly relevant for those reading these manuscripts.…”

Section: When Does It Not Work and Why?mentioning

confidence: 99%