We are halfway through our Artificial Intelligence in Pathology blog series. So far, we’ve explored AI’s opportunities, the critical importance of data, and the process of building AI models. In this article, we get into why validating these tools is a pivotal step—not only to build trust among pathologists but also as a vital quality assurance for patients, ensuring accurate and reliable diagnoses in real-world settings.

An AI medical device is a regulated, healthcare-specific application of AI designed to support medical decision-making and patient care. This regulation, conducted by organizations such as the European Medicines Agency (EMA) and the US Food and Drug Administration (FDA), ensures patient safety and efficacy. The key component in any regulatory process is validation.

Validation ensures that the model’s predictions are accurate, reliable, and generalizable to new, real-world patient samples. It assesses the model’s ability to avoid overfitting, where the model might perform well on training data but poorly on new, unseen data. Validation determines whether the model can be trusted for its intended application in a medical device.

Read the full article here.