These locked algorithms are modified by the manufacturer at intervals, which includes “training” of the algorithm using new data, followed by manual verification and validation of the updated algorithm.
“But there’s a great deal of promise beyond locked algorithms that’s ripe for application in the health care space, and which requires careful oversight to ensure the benefits of these advanced technologies outweigh the risks to patients,” said Gottlieb.
“…machine learning algorithms that continually evolve, often called “adaptive” or “continuously learning” algorithms, don’t need manual modification to incorporate learning or updates. Adaptive algorithms can learn from new user data presented to the algorithm through real-world use.”
For example, as we recently reported, the FDA cleared an algorithm to identify cervical fractures from CT scans which can compare scans to other CT scans and “learn” the images that correlate with fractures, and eventually, be more accurate than a human in detecting cervical fractures from those image.
For traditional software as a medical device, when modifications are made that could significantly affect the safety or effectiveness of the device, a sponsor must make a submission demonstrating the safety and effectiveness of the modifications.
The Proposed Framework
The FDA papers says “the goal of the framework is to assure that ongoing algorithm changes follow pre-specified performance objectives and change control plans, use a validation process that ensures improvements to the performance, safety and effectiveness of the artificial intelligence software, and includes real-world monitoring of performance once the device is on the market to ensure safety and effectiveness are maintained.”
Gottlieb said the agency is considering how an approach that “enables the evaluation and monitoring of a software product from its premarket development to post-market performance could provide reasonable assurance of safety and effectiveness and allow the FDA’s regulatory oversight to embrace the iterative nature of these artificial intelligence products….”
He said the “first step in developing our approach outlines information specific to devices that include artificial intelligence algorithms that make real-world modifications that the agency might require for premarket review. They include the algorithm’s performance, the manufacturer’s plan for modifications and the ability of the manufacturer to manage and control risks of the modifications.”
The agency may also intend to review what’s referred to as software’s “predetermined change control plan.”
“The predetermined change control plan would provide detailed information to the agency about the types of anticipated modifications based on the algorithm’s re-training and update strategy, and the associated methodology being used to implement those changes in a controlled manner that manages risks to patients.”
“Consistent with our existing quality systems regulation, the agency expects software developers to have an established quality system that is geared towards developing, delivering and maintaining high-quality products throughout the lifecycle that conforms to the agency’s standards and regulations.”
