Author

Nathan Abercrombie

Sr. Machine Learning Engineer

As a senior machine learning engineer at Apella, Nathan builds the computer vision systems that ensure the quality, reliability, and scalability of the company’s AI models in real-world clinical environments. His work spans computer vision, event detection, and predictive modeling, with a strong emphasis on measurement, evaluation, and continuous improvement. He develops evaluation pipelines and realtime tooling that enable high-quality training data at scale, helping ensure Apella’s AI systems deliver transparent, trustworthy performance in the operating room.

Articles by Nathan Abercrombie

Always Learning: Why Healthcare AI Models Need Human Expertise

Progress in AI is often associated with reducing the need for human involvement. In healthcare, though, human expertise still plays an important role, particularly in moments when context, judgment, and a deeper understanding of the environment matter most.

Overcoming Complexity: Why Healthcare AI Requires its Own Computer Vision Models

Performance depends on understanding both the workflows that happen every day and the unusual moments that inevitably come up. That provides the context needed to translate what’s actually happening into useful operational data. That’s why Apella built its own computer vision models.

Apella's Ambient AI Was Independently Peer-Reviewed; Here's What the Research Found

A peer-reviewed BMJ Health & Care Informatics study, across more than 100,000 surgical cases, demonstrates what decision-grade ambient AI looks like when performance, validation, and scale are rigorously measured.