As part of our blog series on artificial intelligence and machine learning driven medical devices, we discuss the SaMD Pre-Specification and the Algorithm Change Protocol that FDA has crafted for such devices, in this blog...

1. Initial Premarket Assurance of Safety and
Effectiveness for AI/ML Medical Devices
By: Nikita Angane
As part of our blog series on artificial intelligence and machine learning driven medical devices,
we discuss the SaMD Pre-Specification and the Algorithm Change Protocol that FDA has crafted
for such devices, in this blog.
Before a medical device can be marketed in the U.S. it must be cleared or approved by FDA.
Historically this has only been achieved through the 510(k), De Novo, and Premarket Approval
(PMA) processes, but with the release of the proposed regulatory framework on Artificial
Intelligence/ Machine Learning (AI/ML) based Software as a Medical Device (SaMD), there
could soon be another premarket process.
In order to utilize the ability of AI/ML driven devices to continuously learn from Real-World
Data (RWD) and improve themselves, FDA decided to come up with a new regulatory
framework to regulate the changes as they are post market modifications to the SaMD.
Submitting each change to FDA before implementation would not capitalize on the advantage of
self-improving devices.
Our previous blogs: ARTIFICIAL INTELLIGENCE & MACHINE LEARNING
TECHNOLOGIES and QUALITY SYSTEMS AND GOOD MACHINE LEARNING
PRACTICES, give an overview of the proposed framework and provide a more detailed look
into the first point in FDA’s proposed Total Product Lifecycle (TPLC) approach to regulating
AI/ML-based SaMD, respectively. This blog will take a closer look at the second point in FDA’s
proposed Total Product Lifecycle (TPLC) approach, initial premarket assurance of safety and
effectiveness.
This proposed regulatory framework relies on the principle of a predetermined change control
plan containing SaMD Pre-Specifications (SPS) and an Algorithm Change Protocol (ACP)
which separates it from the premarket review of device modification plans that have taken place
through the De Novo process. The predetermined change control plans would allow companies
to relay what the device’s algorithm is intended to learn and what potential modifications would
therefore occur and how they plan to ensure the device remains safe and effective after the
anticipated modifications are made. The what is to be documented in the SPS and the how is to
be documented in the ACP.1

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The SPS are the anticipated modifications that the manufacturer plans to make during the
software’s actual use. In other words, they are the potential changes that the manufacturer
intends the algorithm to learn and make.i
FDA states the ACP is to be “a step-by-step delineation of the data and procedures to be
followed so that the modification achieves its goals and the device remains safe and effective
after the modification” and to do so should contain four main components: data management, re-
training, performance evaluation, and procedural updates.1
Relying on a change control plan to support modifications depends on many factors. Some of
them are measures to monitor degradation of device performance and risks to the patients,
clinical association of the disease to the input data or assessing the confidence in the ability of
the algorithm to aid in making a diagnosis decision.i
Establishing a predetermined change control plan does not mean anything goes for the
modifications that can be made to the device. FDA wants to enable the true potential of machine
learning, especially as RWD increases in popularity in the healthcare field but needs to ensure a
high level of patient safety. Read our blog, REAL-WORLD DATA TO SUPPORT MEDICAL
DEVICE REGULATORY SUBMISSIONS, for more information regarding what RWD can
bring to the table and how it can affect regulatory decisions. Changes related to the intended use
are allowed if they are not significant and do not greatly impact the risk. In cases where the
modification was not anticipated in the SPS submitted to the FDA or significant modifications
are being made to the intended use, a premarket review will be required. Refer to the guidance
document “Deciding When to Submit a 510(k) for a Software Change to an Existing Device” for
additional information.
You can submit your suggestions or comments on FDA’s Proposed Regulatory Framework for
Modifications to Artificial Intelligence/Machine Learning (AI/ML)-Based Software as a Medical
Device (SaMD) - Discussion Paper and Request for Feedback using the following link:
https://www.regulations.gov/comment?D=FDA-2019-N-1185-0001
For any other questions that you may have about the proposed regulatory framework, please
contact us at 248-987-4497 or info@emmainternational.com.

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i
FDA (Apr 2019) Artificial Intelligenceand MachineLearningin Software as a Medical Device retrieved on 04-10-
2019 from
https://www.fda.gov/MedicalDevices/DigitalHealth/SoftwareasaMedicalDevice/ucm634612.htm