How Do You Get the Data?
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 a hold of sufficient data to train a model, you also need to have other data sets of the same […]
Data Standardization and Integration Into Existing Clinical Workflows II
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 mobile-based apps as well as cloud-based communications. Looking to the future, FHIR framework will be critical for implementation of AI-based […]
Data Standardization and Integration Into Existing Clinical Workflows I
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 tools and methodologies. This is a key concern because data are collected in different methods for different purposes and can […]
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 […]
Artificial Intelligence: The Future of Healthcare?
I have seen the evolution of healthcare over the course of my career, first as a Cardiologist, then as an entrepreneur and investor in healthcare. I have seen universally fatal diseases like hairy cell leukemia be cured with one round of chemotherapy!! If you had a heart attack in 1970, your odds of dying from […]