AI Hype vs. AI Reality at AAOS: What You Need to Know.

Not all “AI” in orthopedics is what it seems.

As you wander the halls of AAOS, “AI” buzzwords will be plastered everywhere—on booths, banners, and brochures. But hold up—don’t buy the hype just yet.

Much of what’s marketed as “AI” is smoke and mirrors, not the real deal. Here’s how to spot authentic AI in orthopedics versus flashy fakes, broken down into three key traits:

1/ Scale Matters: Tens of Thousands of Cases

For complex orthopedic surgeries with tons of variables—say, a tricky spine fusion or joint replacement—reliable AI demands datasets with tens of thousands of cases, if not more. Simpler tasks, like pinpointing a single decision mid-procedure, might get by with less. Ask yourself: Does this “AI” tool have the depth to back its claims? If it’s based on a handful of cases, it’s more marketing than medicine.

2/ Dataset Diversity: The Backbone of Real AI

True AI in orthopedics—think pattern recognition for diagnostics or surgical precision—needs a massive, varied dataset to train on. We’re talking thousands of patient cases and surgical scenarios, capturing diverse anatomies, complications, and procedural twists. If the AI can’t handle real-world variety, it’s not ready for the operating room. Beware of “AI” built on skimpy, narrow data—it’s just a shiny toy.

3/ Human Labeling: The Unsung Hero

Size isn’t everything—quality seals the deal. A top-notch dataset isn’t just big; it’s meticulously curated and labeled by humans who tag every detail, from bone fractures to surgical outcomes. This grunt work is what turns raw data into AI gold. No labels? It’s just noise. Next time you see an “AI” pitch, ask: Who’s doing the labeling? If the company doesn’t employ data curators, their AI is likely a hollow shell.

The Bottom Line

Real AI in orthopedics hinges on robust, diverse datasets—think tens of thousands of well-labeled cases for complex procedures, or smaller but still solid sets for simpler tasks. Quality and quantity go hand in hand. So, when you’re dazzled by AI claims at AAOS, dig deeper. Is it legit, or just a slick sales pitch?

Spot some “AI” ads on the floor? Snap a pic and text us at 512-992-9090—we’ll help you sort the real from the fake!

During AAOS, you are going to see “AI” marketing everywhere.

Beware. Most of the “AI” claims are just hype, not reality.

Below are three traits of “real AI”.

1/ Dataset diversity

In order to leverage AI in pattern recognition for decision making in treating patients or in surgical procedures, a large and diverse medical dataset is required to train the AI model. The dataset should ideally contain a huge number of patients or surgical cases that cover a wide range of variations and complications that can occur with a wide variety of patient anatomy and events during the surgical procedure.

2/ Ten thousand

For complex orthopedic procedures that involve multiple variables and decision points, a dataset with tens of thousands of cases or more may be necessary to develop an accurate and reliable AI model. However, for simpler procedures or for specific decision points within a procedure, a smaller dataset may be sufficient.

3/ Human Labeling

It is important to note that the quality of the orthopedic dataset is equally important as the quantity. The dataset should be well-curated and labeled correctly to ensure that the AI model can accurately learn patterns and make informed decisions. This means hours of humans actually manually labeling attributes. The human labor side of AI is often overlooked. Without labels the dataset is just noise. How many orthopedic companies employ labelers?

Summary

The size of the orthopedic dataset required to leverage AI in pattern recognition for decision making in surgical procedures depends on various factors. For complex orthopedic procedures, a dataset with tens of thousands of cases may be necessary, while for simpler procedures, a smaller dataset may be sufficient. However, the quality of the dataset is equally important as the quantity.