Algorithm Approval Acceleration
The amount of US Food and Drug Administration (FDA) approvals of proprietary medical algorithms powered by artificial intelligence (AI) for image interpretation is “expanding rapidly,” said an AI review article published in Nature Medicine, according to Ana Mulero on the website of the Regulatory Affairs Professionals Society.
Eric Topol, director and founder of the Scripps Research Translational Institute, wrote a research review article that stated that FDA’s AI approvals ranged from one to two per month in 2018. last year. There were only two FDA AI approvals in 2017. According to FDA Commissioner Scott Gottlieb in 2018, the FDA is “actively developing a new regulatory framework to promote innovation” in the AI area.
However, according to Topol, “there have been few peer-reviewed publications from most” of the companies that received FDA AI approvals between 2017 and 2018. He added, “Among the studies that have gone through peer review, the only prospective validation studies in a real-world setting have been for diabetic retinopathy, detection of wrist fractures in the emergency room setting, histologic breast cancer metastases, very small colonic polyps and congenital cataracts in a small group of children.”
Topol mentioned an algorithm that operates an IDx device for diabetic retinopathy that received FDA approval last year as “the first prospective assessment of AI in the clinic.” This IDx device is one of a small number of AI approvals that have been highly praised by FDA officials. There are others, such as an application developed by Viz.ai to detect a possible stroke and two applications in the new Apple Watch that are designed to help patients with atrial fibrillation. These apps garnered FDA approval in 2018.
In 2017 the AI-powered devices that were approved by FDA included AliveCor’s KardiaMobile smartphone app to be used on the Apple Watch to help in atrial fibrillation detection and the Arterys Oncology AI suite.
Additionally, FDA explained its new test plan for the coming phase of FDA’s digital health Pre-Certification (PreCert) pilot program. The gist of PreCert version 1.0 will be to set up processes for software as a medical device (SaMD), which might entail software functions using AI and machine learning algorithms, as established within FDA’s current jurisdiction. FDA said that additional authority could be required beforehand in order to completely implement the PreCert program for other kinds of digital health devices, instead of just first-of-its-kind SaMD.
According to Topol, “The regulatory oversight in dealing with deep-learning algorithms is tricky because it does not currently allow continued autodidactic functionality but instead necessitates fixing the software to behave like a non-AI diagnostic system. Instead of a single doctor’s mistake hurting a patient, the potential for a machine algorithm inducing iatrogenic risk is vast. This is all the more reason that systematic debugging, audit, extensive simulation, and validation, along with prospective scrutiny, are required when an AI algorithm is unleashed in clinical practice. It also underscores the need to require more evidence and robust validation to exceed the recent downgrading of FDA regulatory requirements for medical algorithm approval.”
