The state of artificial intelligence-based FDA-approved medical devices and algorithms: an online database

By S. Benjamens, Pranavsingh Dhunnoo, Bertalan Meskó. npj Digital Medicine, 2020

925 citations recorded by the source corpus.

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 for implementation of AI/ML in daily clinical practice are numerous, especially regarding the regulation of these technologies. Therefore, we provide an 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.

Subfields: Biomedical Engineering, Economics and Econometrics, Health Informatics

View Paper: https://www.semanticscholar.org/paper/0623ccb3694aae29857890809afe5868931a98ec

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Citations in the corpus

Bias in AI-based models for medical applications: challenges and mitigation strategies (2023, neutral): “An AI algorithm can either be locked — once the algorithm is trained, the model provides the same result when the same input is applied — or adaptive 16 .”
The Fallacy of AI Functionality (2022, background): “In fact, healthcare is the ﬁeld where AI product failures appear to be the most visible, in part due to the rigor of pre-established evaluation processes [20, 119, 160, 205].”
Clinical artificial intelligence quality improvement: towards continual monitoring and updating of AI algorithms in healthcare (2022, background): “Indeed, the list of AI/ML-based algorithms approved for clinical use by the United States Food and Drug Administration (FDA) continues to grow at a rapid rate 7 .”
The Role of AI in Hospitals and Clinics: Transforming Healthcare in the 21st Century (2024, background): “For instance, in the United States, the Food and Drug Administration (FDA) is actively working on establishing clear guidelines for AI and machine learning-based medical devices [174].”
Artificial Intelligence for Mental Healthcare: Clinical Applications, Barriers, Facilitators, and Artificial Wisdom (2021, background): “No FDA-approved or FDA-cleared AI applications currently exist in psychiatry (19). a However, such applications have long been envisioned – e.g., the potential of expert systems and computerized therapy (20). r Reasons for the delayed adoption of AI into routine psychiatric practice include the…”
Artificial intelligence in radiology: 100 commercially available products and their scientific evidence (2021, background): “Some AI product overviews have previously been published [14, 15].”