FDA has now cleared more than 500 healthcare AI algorithms

(An updated version this article was posted December 2023 - FDA has now cleared 700 AI healthcare algorithms, more than 76% in radiology)

There are now more than 520 market-cleared artificial intelligence (AI) medical algorithms available in the United States, according to the U.S. Food and Drug Administration (FDA) as of January 2023. The vast majority of these are related to medical imaging.

Here is the breakdown for the number of FDA-cleared algorithms across specialties:

   • Radiology 396
   • Cardiology 58
   • Hematology 14
   • Neurology 10
   • Clinical chemistry 7
   • Ophthalmic 7
   • Gastroenterology and urology 5
   • General and plastic surgery 5
   • Pathology 4
   • Microbiology 4
   • Anesthesiology 4
   • General Hospital 3
   • Orthopedic 1
   • Dental 1

See the complete list of FDA-cleared algorithms here

The first AI algorithm was cleared by the FDA in 1995, and fewer than 50 algorithms were approved over the next 18 years. However, the numbers have increased rapidly in the past decade, and more than half of algorithms on the U.S. market were cleared between 2019 to 2022––more than 300 apps in just four years. Last October, the FDA approved 178 new AI and machine learning (ML) systems. That number is expected to grow rapidly into the future, the FDA has said. 

AI and ML technologies have the potential to transform healthcare by deriving new and important insights from the vast amount of data generated during the delivery of healthcare every day. Medical device manufacturers are using these technologies to innovate their products to better assist healthcare providers and improve patient care, the FDA explained on its website.

Medical imaging makes up the majority of FDA-cleared AI algorithms

"Artificial intelligence is really good at discerning patterns within the data. There has been a lot of work in the medical imaging space, where AI can really help improve diagnostic capabilities with image recognition," explained Julius Bogdan, an AI expert and vice president and general manager of the Healthcare Information and Management Systems Society (HIMSS) Digital Health Advisory Team for North America. 

HIMSS sees the movement toward wider AI adoption as one of the biggest trends in healthcare information technology (IT). The clinical side of AI requires regulatory clearance, and the majority FDA-approved algorithms are imaging related across subspecialties. 

AI's high accuracy of pattern recognition has led to a concentration of market approvals on the imaging side. AI can be trained using thousands or tens of thousands in images that show the various presentations of a specific disease, such as ischemic and and hemorrhagic stroke, specific cancers, breast density or coronary shift plaque morphology. Most of the radiology applications focus on specific subspecialty imaging, such as brain, breast, cardiac, lung and stroke imaging. 

Not surprisingly, several of the cardiology, ophthalmic, surgical, obstetrics/gynecology, gastroenterology and urology, dental and pathology AI systems also involve medical imaging. 

"Today, we live in that quadrant of things humans can do and humans are supervising," explained Keith J. Dreyer, DO, PhD, FACR, American College of Radiology (ACR) Data Science Institute chief science officer. Dreyer also holds the positions of vice chairman of radiology at Massachusetts General Hospital, chief data science and information officer for the departments of radiology for both Massachusetts General Hospital and Brigham and Women's Hospital

"That is all the FDA-approved AI stuff that we see today," Dreyer said. "Even though all these FDA-approved algorithms are using AI, it is not all what most people think of, where it is making a diagnosis or finding a lesion. There is a big variety in the ways to classify AI." 

The areas of AI in imaging include:
   • Diagnostic aids that can automatically identify critical findings.
   • Automation of time-consuming functions such as quantification, contouring and auto complete of text in reports.
   • Workflow improvements and automation.
   • Data mining applications.
   • Clinical decision support for next steps in the patient's care or to ensure imaging exam meets the guidelines.
   • Modality specific AI to iso-center patients, choosing imaging protocols, or speeding MRI exam time.  
   • AI to enhance image reconstructions, to improve image quality, resolution, and identify and fix imaging artifacts.
   • Guidance AI to help imagers get the best possible images, even if they are novice users of the system or are unfamiliar with the anatomy.
   • Automatic anatomical identification, labeling and contouring of organs or specific types of tissue.