FDA Speeds Up Artificial Intelligence Approvals, Review Finds
Posted 10 January 2019 | By
The number of US Food and Drug Administration (FDA) approvals of proprietary medical algorithms that are powered by artificial intelligence (AI) for image interpretation is “expanding rapidly,” according to an AI review article published in Nature Medicine
The research review article
produced by Eric Topol, director and founder of the Scipps Research Translational Institute, found that FDA’s AI approvals ranged from one to two per month last year, compared to a total of just two FDA AI approvals in 2017. FDA Commissioner Scott Gottlieb also indicated
last year that the agency is “actively developing a new regulatory framework to promote innovation” in the AI space.
“Yet there have been few peer-reviewed publications from most” of the companies that received FDA AI approvals between 2017 and 2018, Topol said. “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 pointed to an algorithm that powers 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 among a select few of AI approvals that have been highly touted by FDA officials. Others include an application (app) developed by Viz.ai for the detection of a potential stroke and two applications in the new Apple Watch that are intended to aid patients with atrial fibrillation. These received FDA's OK in 2018.
AI-powered devices from 2017 included AliveCor’s KardiaMobile smartphone app indicated for use on the Apple Watch to aid in atrial fibrillation detection and the Arterys Oncology AI suite. The upwards trend in AI approvals or clearances is reflected in the table below.
In addition, FDA revealed
its new test plan for the next phase of its digital health Pre-Certification (PreCert) pilot program on Monday. The focus of PreCert version 1.0 will be to establish processes for software as a medical device (SaMD), which may include software functions that use AI and machine learning algorithms, within FDA's current authorities. The agency indicated additional authority may be required prior to fully implementing the PreCert program for other types of digital health tools, rather than just first-of-its-kind SaMD.
“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,” Topol wrote. "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."