Artificial intelligence is transforming industries at lightning speed—and healthcare is no exception. From predictive analytics to generative AI tools that help streamline documentation, AI in healthcare is evolving how providers deliver care. But behind every smart AI system is one critical question: How does AI actually learn?
We asked Curtis Helsel, Forcura’s Data Science Engineer, to break it down.
“So in modern AI systems, it's given a large amount of data, and then the model itself will actually guess the output from that data. And based on how close that guess is, it will update its understanding for the future data that it receives.” explains Curtis.
Let’s unpack that.
AI Learns by Making (a Lot of) Mistakes—Fast
AI systems are trained using large datasets—think millions of patient records, clinical notes, or care coordination workflows. A machine learning model processes this data, makes an educated guess, then adjusts based on how accurate that guess was. This process, called training, is repeated until the model becomes reliably accurate.
It's a little like a new clinician learning to assess symptoms—only AI can do it in milliseconds and across thousands of cases.
Generative AI and the Next Frontier
When it comes to generative AI in healthcare, these models don’t just analyze data—they create content. Whether it's summarizing visit notes or drafting patient communications, the more these systems learn from high-quality data, the more effective they become in real-world clinical settings.
At Forcura, we thoughtfully integrate AI into our products to drive smarter, faster care delivery. It’s not just about algorithms—it’s about healthcare innovation that’s trustworthy and scalable.
🔍 This post is part of our AI in Healthcare Blog Series.
💡 Curious about how Forcura is applying generative AI to post-acute care?
Explore more insights from our AI Authority page here.
🎥 Catch Curtis’s full explanation of “How Does AI Learn?” in this quick video.
