Every clinical AI developer understands the importance of monitoring, refining, and improving AI models after they’re deployed. Without effective continuous learning and improvement processes, clinical AI models can’t adapt to new sources of data or evolving medical techniques, and they can’t measure model-generated predictions against actual patient outcomes. As a result, they may become less accurate, and therefore less effective, over time — no matter how sound the models or how much high — quality data was used to train them initially.

This white paper highlights solutions for the main hurdles associated with implementing a continuous improvement lifecycle, such as:

- Outcomes data residing in different siloed systems, applications, and organizations.

- Outcomes data not consistently and properly labeled. Privacy regulations limit how patient data can be used and shared.

- Lag time between initial model prediction and relevant outcomes data become available for continual learning.