Can AI Accurately Code Medical Procedures?

The healthcare industry is always looking for ways to make things more efficient. One area that could use a boost is the process of coding medical procedures. This is a task that takes a lot of time and effort. It involves reading through detailed notes from surgeries and assigning the right codes. This is where large language models (LLMs) come into play. These are advanced AI systems that can understand and generate human language. But can they handle the complex task of coding medical procedures accurately? This is what a recent study set out to find. Three LLMs were put to the test: GPT-4, Gemini, and Copilot. The goal was to see how well they could assign the correct codes to various plastic surgery procedures. The test involved giving the LLMs detailed notes from surgeries and seeing if they could pick the right codes. This is important because plastic surgery covers a wide range of procedures, from simple to complex. So, the LLMs had to show they could handle a variety of tasks. The study focused on a specific type of coding called CPT coding. This is a standard way of coding medical procedures in the U. S. It's used to bill for services and track what procedures are being done. The LLMs had to read through the notes and figure out which codes applied. This is not an easy task, even for humans. The notes can be long and full of technical terms. But the LLMs had to make sense of it all and pick the right codes. The results of the study showed that LLMs have promise, but they're not perfect. They can handle some tasks well, but they struggle with others. This is not surprising. LLMs are trained on large amounts of text, but they don't have personal experiences or feelings. They also don't have the medical knowledge that humans have. So, they can make mistakes. But the study showed that with some tweaks, LLMs could be a big help in coding medical procedures. It's also important to note that the study only looked at plastic surgery procedures. Other types of surgery might have different challenges. But the findings give a good starting point for using LLMs in medical coding. The goal is to make the process faster and more accurate. This could free up time for healthcare workers to focus on patient care. It could also reduce errors in billing and tracking. But more research is needed to see how well LLMs can handle other types of procedures. It's a step in the right direction, but there's still work to be done.