Oh, look at us, we’re so fancy with our artificial intelligence (AI) and machine learning (ML) in medical research. Apparently, we’re on the brink of a revolution in orthopaedic research, thanks to our ability to analyze large, multimodal datasets. But wait, there’s a catch! The healthcare sector is a tricky beast, full of inherent characteristics and barriers that make deploying AI and ML a bit of a challenge.

So, if you’re a researcher looking to dive into the world of AI-intensive studies, you better get your head around the key concepts, biases, and clinical safety concerns. Don’t worry, this article is the first in a series that will hold your hand through the process.

We’re going to dazzle you with examples of how AI can predict disease or injury outcomes, analyze medical images, support clinical decisions, and even create digital twins. But don’t get too excited, we also need to talk about the role of humans in training these AI models. Yes, that’s right, we can’t just let the machines run wild. We need to ensure they’re unbiased, generalizable, and explainable, especially in high-risk clinical settings.

And let’s not forget about the need for expert oversight and clinical safety measures. We wouldn’t want any AI failures now, would we? So, buckle up and get ready for a wild ride through the opportunities and challenges of AI in medicine. Just remember, safety and ethics first! Level of evidence IV.