AI's Ascendance in Medicine: A Timeline
Apr 20, 2023 Cedars-Sinai Staff. Illustration by Kirsten Ulve.
Scientists began laying the groundwork for artificial intelligence (AI) in the early 1950s and were exploring multiple AI medical applications by the 1970s. In the years since, the empowering technology has proliferated.
In Computers and Intelligence, Alan Turing describes the "Turing test," designed to uncover whether computers are capable of human intelligence.
John McCarthy coins the term "artificial intelligence" as the science and engineering of building intelligent machines.
"Shakey," the first robot capable of interpreting instructions, is unveiled by Stanford Research Institute.
Scientists create INTERNIST-1, which uses a powerful ranking algorithm to reach diagnoses.
The National Institutes of Health sponsor the first AI in Medicine workshop at Rutgers University.
"Backward chaining" AI system MYCIN delivers suggested antibiotic treatments for potential bacterial pathogens. The Present Illness Program is introduced to help evaluate edema.
Rutgers University develops the causal-associational network model, which couples statistical pattern recognition and AI for glaucoma consultations.
University of Massachusetts releases DXplain, using inputted symptoms to generate diagnoses for 500 diseases—now expanded to more than 2,600 conditions.
Cedars-Sinai cardiologists debut CorSage, a clinical tool that combines AI and statistical techniques to help physicians identify heart patients who are most likely to suffer another coronary event.
The Pathology Expert Interpretative Reporting System generates pathology reports with nearly 95% diagnostic accuracy.
The Human Genome Project provides a wealth of data on the genetic basis of disease.
IBM creates the open-domain question-answering system Watson. In 2011, Watson wins first place on Jeopardy and, in 2017, neurologists use it to identify RNA-binding proteins altered in ALS.
Pharmabot assists in medication education for pediatric patients and caregivers.
Arterys earns Food and Drug Administration (FDA) approval for a product that analyzes heart MRIs in seconds.
Deep-learning applications screen for diseases ranging from diabetic retinopathy to skin cancer with astonishing accuracy. The FDA approves the first AI-powered device for operating-room use.
The FDA approves the first AI-powered device for cancer diagnosis as well as a deep-learning algorithm for interpretation of brain MRIs.
Cedars-Sinai establishes the Division of Artificial Intelligence in Medicine, led by Sumeet Chugh, MD, the Pauline and Harold Price Chair in Cardiac Electrophysiology Research, who relies on AI and population-wide data to demystify susceptibility to cardiac arrest.
Google DeepMind uses AI to predict a protein’s 3D structure from its amino-acid sequence, solving one of biology’s greatest challenges.
Established under the leadership of Jason H. Moore, PhD, the Department of Computational Biomedicine bolsters Cedars-Sinai’s research-computing infrastructure.
The FDA authorizes 91 AI-powered devices. One, the EchoGo Heart Failure tool, detects heart failure from a single echocardiogram.