This article is part five of the ongoing series: AI Prediction, AI Prescription and Causation in Medicine. When transforming predictive models into prescriptive ones, three important considerations must be considered, explains CUNY Data Science Prof Scott Burk.

On day 10, the Yuan’s voyage of intelligent exploration steers for Texas, where everything is larger than life and folks are right friendly - its very name comes from the Caddo Indian word táysha (friends). The 16th century Spanish Conquistadors who first barged into the area were anything but: They attacked and sacked Caddo towns. Spain’s grip slipped when Mexico declared its independence in 1821. Settlers from the infant US led by the Virginian Stephen Austin later overwhelmed the local Spanish-speaking Tejanos by sheer weight of arrivals. Following the epic Battle of the Alamo (1836), the bellicose Anglos wrested Texas away from them in 1837, then joined the US in 1845, only to secede again a mere 16 years later in an even bloodier war for independence - unavailing this time. As with AI, events unfold at warp speed in the Lone Star State, where cowboys in ten-gallon hats morphed overnight into oil barons in homburgs. From its capital, a humming innovation hub named for the ‘Father of Texas,’ CUNY Data Science Prof. Scott Burk posits a future in which humanity and AI are friends, helpmeets making up each other’s shortcomings for the benefit of all.

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.