This recent post is specifically about startups applying machine learning to radiology. However the key points apply broadly to AI startups.
The majority if not most of the startups operating in this field are focused on solving very narrow clinical problems based on limited and biased training datasets and are heavily focused on image pixels rather than healthcare’s big picture; this will refrain them from developing scalable & clinically useful products and building profitable and successful companies.
Companies that are building algorithms to detect one or few radiological abnormalities on one medical imaging modality are building features rather than products.
We need to think more about:
- The overall system utility in production rather than just focusing on the metrics of a specific model seen on a data scientists machine
- How to recognize and overcome the limitations to generalizability that come with our current training datasets.
- Starting our product development efforts with a clear understanding the users biggest problems rather than starting with the technology and looking for the shortest path to some point application of that technology.