[{"innerBlocks":[],"name":"core\/freeform","postId":830,"blockType":{"name":"core\/freeform","keywords":[],"attributes":{"content":{"type":"string","source":"raw"},"lock":{"type":"object"}},"providesContext":[],"usesContext":[],"selectors":[],"supports":{"className":false,"customClassName":false,"reusable":false},"styles":[],"variations":[],"apiVersion":3,"title":"Classic","description":"Use the classic WordPress editor.","category":"text"},"originalContent":"The American Medical Informatics Association wants the Food and Drug Administration to improve its conceptual approach to regulating medical devices that leverage self-updating artificial intelligence algorithms.

The FDA sees tremendous potential in healthcare for AI algorithms that continually evolve\u2014called \u201cadaptive\u201d or \u201ccontinuously learning\u201d algorithms\u2014that don\u2019t need manual modification to incorporate learning or updates.

While AMIA supports an FDA discussion paper on the topic released in early April, the group is calling on the agency to make further refinements to the Proposed Regulatory Framework for Modifications to Artificial Intelligence\/Machine Learning (AI\/ML)-Based Software as a Medical Device (SaMD).

\u201cProperly regulating AI and machine learning-based SaMD will require ongoing dialogue between FDA and stakeholders,\u201d said AMIA President and CEO Douglas Fridsma, MD, in a written statement. \u201cThis draft framework is only the beginning of a vital conversation to improve both patient safety and innovation. We certainly look forward to continuing it.\u201d

Also See<\/b>: FDA mulls new regulatory framework for AI-based medical devices<\/a>

In particular, AMIA sent comments on the framework<\/a> to the agency, recommending improvements in four areas: continuously learning versus \u201clocked\u201d algorithms; new data inputs\u2019 impact on algorithms\u2019 outputs; cybersecurity in the context of AI\/ML-based SaMD; and evolving knowledge about algorithm-driven bias.

When it comes to learning versus locked algorithms, AMIA told the FDA that \u201cwhile the framework acknowledges the two different kinds of algorithms\u201d it is concerned that the framework is \u201crooted in a concept that both locked and continuously learning SaMD provides opportunity for periodic, intentional updates.\u201d

Although AMIA\u2019s letter to the agency voiced appreciation for the fact that the framework accounts for new inputs into a SaMD\u2019s algorithm, the group said it is \u201cconcerned that a user of SaMD in practice would not have a practical way to know whether the device reasonably applied to their population, and therefore, whether adapting to data on their population would be likely to cause a change based on the SaMD\u2019s learning.\u201d

In addition, AMIA claimed that the framework \u201cfails to discuss how modifications to SaMD algorithms may be the result of breaches of cybersecurity and the need to make this a component of periodic evaluation\u201d and that the FDA should \u201cconsider how cybersecurity risks, such as hacking or data manipulation that may influence the algorithm\u2019s output, may be addressed in a future version of the framework.\u201d

Finally, the group recommended that the agency develop guidance about how and how often developers of SaMD-based products test their products for algorithm-driven biases.","saveContent":"The American Medical Informatics Association wants the Food and Drug Administration to improve its conceptual approach to regulating medical devices that leverage self-updating artificial intelligence algorithms.

The FDA sees tremendous potential in healthcare for AI algorithms that continually evolve\u2014called \u201cadaptive\u201d or \u201ccontinuously learning\u201d algorithms\u2014that don\u2019t need manual modification to incorporate learning or updates.

While AMIA supports an FDA discussion paper on the topic released in early April, the group is calling on the agency to make further refinements to the Proposed Regulatory Framework for Modifications to Artificial Intelligence\/Machine Learning (AI\/ML)-Based Software as a Medical Device (SaMD).

\u201cProperly regulating AI and machine learning-based SaMD will require ongoing dialogue between FDA and stakeholders,\u201d said AMIA President and CEO Douglas Fridsma, MD, in a written statement. \u201cThis draft framework is only the beginning of a vital conversation to improve both patient safety and innovation. We certainly look forward to continuing it.\u201d

Also See<\/b>: FDA mulls new regulatory framework for AI-based medical devices<\/a>

In particular, AMIA sent comments on the framework<\/a> to the agency, recommending improvements in four areas: continuously learning versus \u201clocked\u201d algorithms; new data inputs\u2019 impact on algorithms\u2019 outputs; cybersecurity in the context of AI\/ML-based SaMD; and evolving knowledge about algorithm-driven bias.

When it comes to learning versus locked algorithms, AMIA told the FDA that \u201cwhile the framework acknowledges the two different kinds of algorithms\u201d it is concerned that the framework is \u201crooted in a concept that both locked and continuously learning SaMD provides opportunity for periodic, intentional updates.\u201d

Although AMIA\u2019s letter to the agency voiced appreciation for the fact that the framework accounts for new inputs into a SaMD\u2019s algorithm, the group said it is \u201cconcerned that a user of SaMD in practice would not have a practical way to know whether the device reasonably applied to their population, and therefore, whether adapting to data on their population would be likely to cause a change based on the SaMD\u2019s learning.\u201d

In addition, AMIA claimed that the framework \u201cfails to discuss how modifications to SaMD algorithms may be the result of breaches of cybersecurity and the need to make this a component of periodic evaluation\u201d and that the FDA should \u201cconsider how cybersecurity risks, such as hacking or data manipulation that may influence the algorithm\u2019s output, may be addressed in a future version of the framework.\u201d

Finally, the group recommended that the agency develop guidance about how and how often developers of SaMD-based products test their products for algorithm-driven biases.","order":0,"get_parent":{},"attributes":[],"attributesType":{"content":{"type":"string","source":"raw"},"lock":{"type":"object"}},"dynamicContent":""}]

AMIA calls on FDA to refine its AI regulatory framework

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