We can offer to help here, but it’s later something that alignment that can work with the governor or with you, your team. We can see how, what kind of opportunity we can have.

Yeah, because we are working with the medical problem for some time, and we have very good AI engine using our team. We have AI talent. Right now, it’s about there is something that we can offer.

I’d like to have some open discussion for how AICS can help.

Also, we’re doing risk prediction. Also, we have good performance, and even Google doing the same project. We are on par with their results.

We also value our AI quality. All our model, we will have some benchmark. We make sure we beat the state of the art for ICD-10.

Right now, because you see there are medical records, you can see they are, it’s just free text. We use machine learning, use NLP to check the medical terms for the data. Also, we can…

We are actually doing, like providing Google search for the hospitals.

This is something we are tackling right now with the hospital.

When they are doing research, they could use the hospital data to do cohort study, or also, if they want to take care of one patient for cancer treatment, they can look through the past data and to find out a more precise, more personalized treatment for that patient.

From the hospital side, they also want to apply the real-world data to solve their problem. When AI look through their system, we are doing auto-tagging for them to organize their unstructured data to something can be searchable and can be computable.

We have this simple warning system for doctors to see if they have some potential patients they need to take care of. This is something already deployed to one hospital.

For COVID-19, we read through the papers published for what kind of the symptoms could be related to COVID-19.

If they work with us, we will try to organize a data platform for machine learning. We are not just tackle the ICD-10 coding problem. We are actually tackling a bigger problem. I will explain that later.

Right now, we’re also solving one of their pain points from the hospitals. All the hospitals, they want to do AI, but the data, they are not organized or not cleaned.

It’s a closed feedback loop. We would be getting better and better. Also, we use attention model to help the AI to be expandable. This is something we are already work with hospital and being applied in their daily practice.

Oh, yeah.

There are three key parts. This is a real-world example. The first part is that we use AI to look at the medical data. We could help the doctor to make better decision for choosing ICD-10.

We have a demo video, so we can take a look at it.

We only work with the written part and also the blood tests. Something like biomarkers, we combine those data to make a decision.

For the hospital operation, doctors need to submit a code. It’s called International Classification of diseases.

When you visit doctor, you tell him what kind of uncomfortable feelings you have, and what symptoms you have, doctor would record what you said and maybe do some lab testing. We will combine all this data, like from the medical records and the blood test features, and we’ll help ...

We helped hospital to build a heterogeneous medical big data platform, then we can have a lot AI applications happening. The first project we’re taking on is disease classification.

Also, our vision, we want to transform the data within the hospital, the medical data, into collaborative knowledge. For doctors, they can provide better treatment plans to patients with data-driven decision support.

We need to use AI to transform those data.

The first problem that we encountered is that within the hospital, a lot of unstructured data. We must apply AI to transform the unstructured data to something which computer can compute.

This started from last year. Since July, we have the opportunity to work with one hospital. We are AI team, and the hospital has data.

I’m going to introduce the smart medical.

We still have engineering systems…