Jeff Smith
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AI & ML, Blog Series: Artificial Intelligence & Machine Learning, Electronic Health & Medical Records, Health Data, Health Innovation, Health IT Certification
Increasing the Transparency and Trustworthiness of AI in Health Care
Jeff Smith | April 13, 2023
This is part five of a blog series on predictive models, artificial intelligence (AI) & machine learning (ML) in health. We encourage readers to (re)visit the four previous blog posts for important context to what follows.
Through a series of blog posts over the last year, we’ve described our understanding of the current and potential uses of predictive models and machine learning algorithms in health care, and the role that ONC can play in shaping their development and use.
Blog Series: Artificial Intelligence & Machine Learning, Health Data, Health Innovation, Health IT Certification, Privacy and Security
Back to the Future: What Predictive Decision Support Can Learn from DeLoreans and The Big Short
Jeff Smith | December 13, 2022
In the third blog in our series on artificial intelligence (AI) and machine learning (ML)-driven predictive models (data analytics tool or software) in health care, we discussed some potential risks (sometimes referred to as model harms) related to these emerging technologies and how these risks could lead to adverse impacts or negative outcomes. Given these potential risks, some have questioned whether they can trust the use of these technologies in health care.
Read Full Post.Blog Series: Artificial Intelligence & Machine Learning, Electronic Health & Medical Records, Health Data, Privacy and Security
Two Sides of the AI/ML Coin in Health Care
Jeff Smith | October 19, 2022
As we’ve previously discussed, algorithms—step by step instructions (rules) to perform a task or solve a problem, especially by a computer—have been widely used in health care for decades. One clear use of these algorithms is through evidence-based, clinical decision support interventions (DSIs). Today, we see a rapid growth in data-based, predictive DSIs, which use models created using machine learning (ML) algorithms or other statistical approaches that analyze large volumes of real-world data (called “training data”) to find patterns and make recommendations.
Read Full Post.Blog Series: Artificial Intelligence & Machine Learning, Health Data, Health Innovation, Health IT Certification, Privacy and Security
Minimizing Risks and Maximizing Rewards from Machine Learning
Jeff Smith | September 7, 2022
When talking about artificial intelligence (AI) today, people are usually referring to predictive models—often driven by machine learning (ML) techniques—that “learn” from historic data and make predictions, recommendations, or classifications (outputs) which inform or drive decision making. The power of ML is in its enormous flexibility. You can build a model to predict or recommend just about anything, and we have seen it transform many sectors.
Read Full Post.AI & ML, Blog Series: Artificial Intelligence & Machine Learning, Electronic Health & Medical Records, Health Data
Getting the Best out of Algorithms in Health Care
Jeff Smith | June 15, 2022
The same basic technology that can predict what movie you might want to watch, what song you might want to listen to, or what item you might want to buy online, can also predict the onset of diseases, forecast costs of care, and recommend treatment options for your doctors, nurses, and pharmacists.
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