My guest today is Dr. Jacob Jaremko, and we are discussing a topic that may change how we diagnose developmental dysplasia of the hip around the world: artificial intelligence.

For decades, hip ultrasound has been central to early detection. Yet despite its value, it remains highly dependent on the person performing and reading the scan. Small differences in image acquisition, landmark identification, probe position, and measurement thresholds can lead to very different interpretations—and, ultimately, different treatment decisions.

But what if we could standardize that process?

Recent advances in artificial intelligence, machine learning, and three-dimensional ultrasound are challenging many of our assumptions about how hip dysplasia should be diagnosed. New multicenter studies suggest that AI-assisted ultrasound can approach expert-level diagnostic accuracy, improve reproducibility, and expand access to high-quality screening far beyond specialized centers.

In this episode, we discuss the latest evidence behind AI-assisted hip ultrasound, the role of 3D imaging, how these tools may reduce variability, and whether the future of DDH screening will focus less on measuring angles and more on predicting risk and guiding clinical decisions.

We also explore practical implementation: scan quality, handheld ultrasound, global screening programs, and the potential impact on patients and families worldwide.