Your next doctor, powered by artificial intelligence
Although every technology product released today seems as if it is “powered by artificial intelligence,” the actual AI revolution is ahead of us. When it arrives, it will be on par with the industrial revolution in changing our lives, especially in the world of medicine.
John McCarthy, a legendary computer scientist, coined the term “AI” in 1955. He defined it as “the science and engineering of making intelligent machines, especially intelligent computer programs. It is related to the similar task of using computers to understand human intelligence, but AI does not have to confine itself to methods that are biologically observable.”
There are two broad types of AI: general and narrow.
Think of general AI as replacing humans (something that is a long way off — think science fiction such as Westworld or Ex Machina), while narrow AI automates some of our tasks (think Siri or Cortana), something that has been happening for years and is set to change entire industries such transportation.
Machine learning is a subset of artificial intelligence. A program is said to have “learned” if it can improve its performance on a task from past experience or data without being explicitly programmed to do so. The standard computer program has hard-coded conditions and responses. For example, a tic-tac-toe program might be programmed to place its X in front of its opponent’s two Os, or in the center spot if it’s available. However, a tic-tac-toe algorithm that uses machine learning may start off with just knowing the basic rules of tic-tac-toe, and then determine its own strategies to win.
Machine learning is poised to revolutionize medicine and health care delivery. Crowdsourcing health care data will also accelerate the incorporation of machine learning into health care, even improving the speed at which research is done. One example is Apple’s ResearchKit, which is collecting data on Parkinson’s disease patients’ assessment of their condition. In a study conducted by Sage Bionetworks and Rochester Medical Center, participants downloaded the app and completed tasks that measured their performance in balance, speech, memory and dexterity over time. Information was collected through their iPhones’ data sensors. This is a vast improvement over the typical model of patients coming into a facility, getting hooked up to expensive and complicated machinery and performing tasks in an artificial environment. These data are already being used to measure the association between medication use and symptom improvement through machine-learning algorithms.
Pharmaceuticals are now reflecting the effects of machine learning. Today, the drug-discovery process takes many years.
Posted on 7wData.be.