While data is an essential component in developing an AI diagnostic [see blog 2], its power is only realized with an algorithm that analyzes and learns from the data.

An AI diagnostic model consists of a set of instructions that process a whole slide image (WSI) and make a prediction, such as screening for microsatellite instability (MSI). To create this model, it must learn from examples, use different layers of understanding (like a pathologist zooming in and out), and make mistakes before learning to improve – not unlike a human studying a new task.

But enough of this abstract talk of models. This blog will illustrate how multiple models come together in building an AI diagnostic like Owkin’s MSIntuit CRC.

Read the full article here.