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Is the FDA ready for artificial intelligence and machine learning?

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Software as a Medical Device (SaMD) – FDA’s AI/ML Plan

The greatest advantage of artificial intelligence/machine learning (AI/ML) is its ability to learn, reason, and resolve problems from real-world use and experience and add to its capability to improve performance. This ability for AI/ML to train itself from real-world feedback and improve its performance makes it unique.

In February 2020, the FDA for the first time approved an AI-assisted cardiac ultrasound guidance software named ‘Caption Guidance’. This device is notable not only for its pioneering AI but also for its flexibility to incorporate future modifications. Following this approval, multiple workshops and programs were held by the FDA with an aim of seeking relevant opinions and feedback from stakeholders who are active in the healthcare domain in order to improve its activities in this space.

The FDA has recently launched the Digital Health Center of Excellence (DHCE) to regulate digital health products. On Jan 18, 2021, through this center, FDA presented the ‘AI/ML-based Software as a Medical Device (SaMD)’ action plan in 5 sections that address stakeholders’ feedback. Although completely focused on SaMD, it is expected that it would also be relevant to other medical device areas like Software in a Medical Device (SiMD).

The Action Plan identifies several opportunities for SaMD developers to engage the FDA and its regulatory framework as the field of SaMD gains momentum. While this Action Plan is the roadmap for future regulations, an operational framework is expected to be released soon.

 

Highlights of the Action Plan

Tailored Approach 

It is expected that the regulatory framework would allow for post-market modifications in  SaMD based on the establishment and utilization of Pre-specifications (SPS) and an Algorithm Change Protocol (ACP). This is included in the action plan as ‘Determined Change Control Plan’. SaMD or AI/ML developers are expected to submit comments on the draft guidance that is expected to be released later in 2021.

 

GMLP – Good Machine Learning Practice

The FDA aims to encourage harmonization of the GMLP development through consensus of standards and community initiatives. It supports the development of GMLP in evaluating and improving the algorithms used in ML tools. The GMLP program of the FDA would also be pursued in collaboration with the Medical Device Cybersecurity Program within the FDA.

 

Patient-centric approach and transparency

 

Algorithm transparency

FDA supports scientific efforts in developing methods that evaluate the robustness of software algorithms especially when it comes to ensuring that AI/ML devices are suitable for all types of population irrespective of racial and ethnic diversity. These efforts include research collaborations with the Centers for Excellence in Regulatory Science and Innovation (CERSI) at UCSF, Stanford University, and Johns Hopkins University.

 

Real-world performance

Real-world data collection, its monitoring, and analysis are important concepts in the proposed regulatory framework. Through its framework, the agency has introduced mechanisms that the manufacturers would be able to leverage while working on software modifications in order to mitigate the risks involved and build a favorable benefit-risk profile of the device during marketing authorization submission.

There are also plans to monitor real-world performance by utilizing pilot projects on a voluntary basis and use learnings to validate the real-world performance parameters.

 

Next steps for developers of SaMDs

 

#SaMD #artificialintelligence #machinelearning #FDA #SiMD

 

Sources: 

https://www.fda.gov/news-events/press-announcements/fda-authorizes-marketing-first-cardiac-ultrasound-software-uses-artificial-intelligence-guide-user

https://www.fda.gov/files/medical%20devices/published/US-FDA-Artificial-Intelligence-and-Machine-Learning-Discussion-Paper.pdf

https://www.fda.gov/media/145022/download

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