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.
The potential for ML and related technologies in health care is exciting. For instance, the National Academy of Medicine (NAM) described ML and other forms of AI as having the potential to represent the “payback” of using health IT, “by facilitating tasks that every clinician, patient, and family would want, but are impossible without electronic assistance.”
ONC plays an active role in making this “payback” possible. Much of the health data that fuels ML and AI applications is generated by certified health IT and is underpinned by technical standards and specifications required through the ONC Health IT Certification Program (“Certification Program”). We are excited about the enormous potential these tools could have to improve health care, but we are also aware of potential risks, challenges, and unmet needs… Read the full article here.
