We have been examining the issues of obtaining data (enough of it! and high quality) and preparing that data to be used in training and validating models. One emerging way to deal with the issue...

Transparency of data and AI algorithms is also a major concern. Transparency is relevant at multiple levels. First, in the case of supervised learning, , the accuracy of predictions relies heavily on the accuracy of...

In the final installment in this series about federated AI, we wrap up the discussion around the potential benefits of federated AI in developing good models in healthcare. Remember that this extensive discussion of federated...

We have been examining the topic of data and its importance to developing and running AI algorithms now for several weeks. When it comes to AI, the topic of data can not be discussed enough!...

In our last discussion, we tackled the issue of data as the bloodline for developing AI algorithms for various applications in healthcare. We also discussed how difficult it is to get that data and make...

Data to AI is what oil has been to the world economy for the last century. Unless you have plenty of it, you will not be able to get far! It's not enough to get...

There is an emerging solution for standardizing healthcare data. FHIR utilizes a set of modular components, known as ‘Resources,’ which can be assembled into working systems that will facilitate data sharing within the EHR and...

Data standardization is critical for aggregating data from different sources to train and use AI algorithms. Data standardization refers to the process of transforming data into a common format that can be understood across different...

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...

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...

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...

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...

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...