This article is part four of the ongoing series: AI Prediction, AI Prescription and Causation in Medicine. Predictive and prescriptive models both have uses but transforming models into prescriptive ones leads to more valuable insights, explains CUNY data science Prof Scott Burk.

This third article in the AI Predictions, AI Prescriptions and Causation in Medicine series addresses questions foremost in healthcare: How likely is the occurrence of events? What must be done to ensure certain outcomes ensue? Predictive AI and prescriptive AI hold the answers.

This article is part two of the ongoing series: AI Prediction, AI Prescription and Causation in Medicine. Just as correlation and causation are often conflated, so too are predictive and prescriptive models. Data science expert and It’s All Analytics founder Scott Burk sheds light on the key differences, which are most applicable to which situations, and how to avoid misapplications.

This is an introduction of a nine-part series on predictive and prescriptive AI and ML models in medicine. It introduces key terminology, shows how analytics are used, and the differing levels of human intervention needed, and highlights how clinicians working with AI and ML achieve optimal results, as data science expert and It’s All Analytics founder Scott Burk expounds.

AI and analytics expert Scott Burk took a close look at the true nature of models and how they work in his first article in this series. He now turns his focus on algorithms, questioning what precisely they are, and what their key function is in many technical fields.

Talk about models has always abounded, especially in recent years. AI models, ML models, statistical models, and others. People often use these terms in a very general sense, but what is a model, actually? The world deals with models unconsciously, all the time. Without them, people would have a hard time navigating their way through life.