At the beginning of the artificial intelligence (AI)/machine learning (ML) era, the expectations are high, and experts foresee that AI/ML shows potential for diagnosing, managing, and treating a wide variety of medical conditions. However, the obstacles to the implementation of AI/ML in daily clinical practice are numerous, especially regarding the regulation of these technologies. Therefore, we provide insight into the currently available AI/ML-based medical devices and algorithms that have been approved by the US Food & Drugs Administration (FDA). We aimed to raise awareness of the importance of regulatory bodies, clearly stating whether a medical device is AI/ML based or not. Cross-checking and validating all approvals, we identified 64 AI/ML-based, FDA-approved medical devices and algorithms. Out of those, only 29 (45%) mentioned any AI/ML-related expressions in the official FDA announcement. The majority (85.9%) was approved by the FDA with a 510(k) clearance, while 8 (12.5%) received de novo pathway clearance and one (1.6%) premarket approval (PMA) clearance. Most of these technologies, notably 30 (46.9%), 16 (25.0%), and 10 (15.6%) were developed for the fields of Radiology, Cardiology, and Internal Medicine/General Practice respectively. We have launched the first comprehensive and open-access database of strictly AI/ML-based medical technologies that have been approved by the FDA. The database will be constantly updated.
Those "in the know" have already assessed the development of AI/Machine Learning in the healthcare universe. The developments are akin to a black hole with immense gravitational pull, threatening to consume us and our ability to survey and judge their reliability and utility in the practice of medicine. Everyone is announcing their intention to use it, PHARMA, Healthplans, Government (CMS), and whoever has access to the world wide web.
There is some hope as long as we keep our eyes on the ball. The patient's welfare is at stake. Many preceding devices have not been evaluated. Potential users must perform due diligence prior to implementing these as-yet unproven systems.
The 2010s have brought a rise in the number of studies and papers discussing the role of artificial intelligence (AI) and machine learning (ML) in medicine and healthcare (AI/ML). The number of life science papers describing AI/ML rose from 596 in 2010 to 12,422 in 2019. While we are at the beginning of the AI/ML era, the expectations are high and experts foresee that AI/ML shows potential for diagnosing, managing, and treating a wide variety of medical conditions1.
Indeed, AI/ML-based technologies have been shown to support several medical specialties from radiology oncology to ophthalmology, and general medical decision-making ML models have been shown to reduce waiting times improve medication adherence customize insulin dosages, or help interpret magnetic resonance images9, among others.
Despite its promise, the obstacles to the implementation of AI/ML in daily clinical practice are numerous. These include issues with transparency surrounding these software programs, the inherent bias in the data they are fed, and how secure they are. A crucial element shaping these obstacles is regulating such technologies. The very use of the term AI requires further clarification, as multiple subtypes have been proposed, and its meaning can be vague. For the sake of further investments and the public image, companies tend to overuse the term AI, when in fact they have developed algorithms that are not AI/ML-based per se.
List of AI/ML systems pending approval. as of 01/2023
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