Data access laws and Regulatory issues

The GDPR will affect AI implementation in healthcare in several ways. First, it requires explicit and informed consent before any collection of personal data. Informed consent has been a long-standing component of medical practice (unlike in social media or online- based marketing), but having to obtain informed consent for any collection of data still represents […]

Obtaining Data to Train AI Models

To create large and diverse datasets used for training algorithms that will perform as planned in clinical practice, you need data from multiple institutions. This will minimize the chance of using data that is too skewed toward a certain population and introducing bias into the algorithm. Not only are data necessary for initial training, a continued […]

DATA AS THE BUILDING BLOCK OF ARTIFICIAL INTELLIGENCE

If there is one issue that needs to be front and center in AI will fulfill its potential, it is the issue of data: getting enough of it to train the algorithms, having a steady flow of it when you implement the algorithms in the real world, having it be representative of the target patient […]

History of AI

Artificial intelligence technology has been “around” for some 80 years. Although it has gained significant traction recently and its applications are transforming almost every industry in the past few years,  its foundations were created around the time of the second World War. Alan turing is considered one of the pioneers in computing and artificial intelligence […]

What is AI?

It’s important to remember AI isn’t magic (or robots coming to replace your doctor) — it’s just math. Terms like “machine learning” and “deep learning” are simply ways of explaining statistics-based computer algorithms. These algorithms need a lot of data to identify patterns and become powerful prediction tools. AI refers to multiple technologies that can be […]