technology

We target 99% accuracy, always.



Currently our Chest Radiography and Mammography solution shows 97-99% and 97% accuracy, respectively.
Lunit’s goal is to always achieve 99%.

And we use our unique, state-of-the-art AI training technology to achieve unprecedented accuracy.

Our AI-centric approach
is what makes our technology exceptional.



Lunit’s research team is committed to addressing the fundamental challenges of AI.
We define general AI problems from domain-specific issues and solve it in our own way.

Our findings during this journey have been published at top-tier AI conferences and journals, gaining international attention.
We wish to continue our contribution to the AI society by pushing the boundaries further.

We use “Human in the loop” simulation.



Annotation is important in training AI with medical data.
The reality in the medical AI world is that it is generally not feasible to have high quality annotation for millions of data.
Traditionally, physicians pick data for annotation, after which, the annotated dataset is used for training.

The problem is that many of these annotations often contain redundant or trivial examples,
which makes this approach not the best particular method in improving the performance of AI.

Our AI actively suggests the cases that it perceived as "most uncertain" within each given dataset.
These cases are then provided with confirmed annotations from our physicians. Think of it as, "learning from mistakes."

We want to make the most of the acquired data.



Data is flooding in, but AI is still data-hungry.
Finding the most effective way to annotate a vast amount of medical data is one thing - making use of all the rest is another.
We want our AI to learn from all the data, but the budget for annotations is limited.

To solve this issue, we employ unsupervised methods that exploit what the AI thinks it knows about the unlabeled data.
After all, our AI learns by itself with unlabeled data, and learns by asking the physicians for uncertain data.

Our AI share knowledge between different tasks.



Once you speak French, it is easier to learn Spanish.
We believe that the various tasks within medical image analysis share primitive knowledge.
By explicitly guiding the AI training process to find and share the common knowledge in between different areas of expertise,
we can equip our AI with more robust and scalable knowledge.