FDA Defines Key AI Terms for Industry: Releases AI and Digital Health Glossary

The FDA has released a new Digital Health and Artificial Intelligence Glossary, designed to help clarify the increasingly complex terms used in the world of artificial intelligence (AI), machine learning (ML), and digital health.

AI-Driven Terms You Should Know

While the glossary covers a wide range of digital health concepts, some of the most thought-provoking and future-focused terms are related to AI. Here are a few that stand out:

Artificial General Intelligence (AGI): While most of the AI used today is “narrow,” meaning it can perform specific tasks like diagnosing diseases or analyzing images, AGI refers to a more advanced form of AI that can understand, learn, and apply knowledge across a wide range of tasks—just like a human. While AGI is still theoretical, its potential for healthcare is enormous, from fully autonomous surgeries to personalized care that adjusts to patients’ real-time needs
Black Box Algorithm: This refers to AI systems that make decisions or predictions without revealing how those decisions were made. In healthcare, a black box algorithm could diagnose a disease or recommend a treatment, but without transparency, it’s hard for doctors and patients to trust the results. One of the big debates in AI today is how to balance the power of these algorithms with the need for explainability.
Federated Learning: This method allows AI to be trained on data from multiple sources without those sources needing to share their raw data. For example, hospitals could use federated learning to collectively train an AI model on patient data, improving diagnostic algorithms while maintaining patient privacy. This is especially important in healthcare, where data security and confidentiality are paramount.
Neural Networks: Inspired by the human brain, neural networks are a key part of AI. They consist of interconnected layers of “neurons” that process data in ways that mimic human learning. In healthcare, neural networks are used in everything from analyzing medical images to predicting patient outcomes, and they have the potential to revolutionize diagnostics by identifying patterns too subtle for humans to detect.
Supervised vs. Unsupervised Learning: These are two major types of machine learning methods. Supervised learning uses labeled data to “teach” AI systems—think training an AI to recognize tumors by feeding it thousands of labeled scans. In contrast, unsupervised learning doesn’t rely on labeled data; the AI learns to identify patterns and clusters on its own. Unsupervised learning could open the door to discovering new diseases or unknown patient subgroups by letting AI analyze massive datasets without human intervention.

Educational, Not Regulatory!

The FDA stresses that this glossary is for educational purposes only. It’s not official guidance, and it doesn’t come with any legal requirements or recommendations. The glossary doesn’t affect regulations under the Federal Food, Drug, and Cosmetic Act. Instead, it’s a handy tool to help everyone better understand the technical language surrounding AI and digital health innovations.

Why these terms matter for Industry!

As AI continues to evolve, it’s increasingly influencing everything from clinical decision support systems to predictive analytics in healthcare. Understanding key concepts like black box algorithms and federated learning is crucial for the industry to keep pace with the rapid changes AI is bringing.

For example, if a healthcare startup is developing a black box AI tool, it will be essential for doctors, developers, and regulators to know what that means—and how it affects transparency and trust in medical decisions. Or, if researchers are using federated learning to train AI on patient data from different hospitals, everyone involved needs to understand how this protects privacy while enabling better healthcare outcomes.

In Brief

By releasing this glossary, the FDA isn’t just offering a set of definitions—it’s helping to bridge the gap between healthcare professionals and AI developers. As terms like AGI and neural networks become more common, the glossary serves as a valuable resource to ensure that everyone—from clinicians to AI engineers to regulators—can communicate effectively and work together to harness the power of AI responsibly.

This glossary is just a starting point. As AI continues to disrupt and improve healthcare, new terms and concepts will emerge, and the FDA’s focus on education will help ensure that the industry stays informed, aligned, and innovative.

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