Diagnosis
AI milestones in diagnosis, part of medicine and health.
5 milestones
CURIAL: An AI System to Detect COVID-19 in Emergency Department Patients Using Routine Blood Tests and Vital Signs
In July 2020, researchers at the University of Oxford, led by Dr Andrew Soltan and Professor David Clifton, announced CURIAL, a machine-learning model trained on routine blood tests and vital signs from 115,000 hospital presentations that could identify COVID-19 patients in emergency departments within one hour and with accuracy exceeding 90%.
PUFF expert system interprets pulmonary function tests at Stanford
In 1983, Janice S. Aikins, John C. Kunz, and Edward H. Shortliffe of Stanford University published a description of PUFF, a rule-based expert system that automated interpretation of pulmonary function test data at Pacific Medical Center in San Francisco, producing physician-reviewed diagnostic reports without manual analysis.
CADUCEUS Medical Expert System (Pople, University of Pittsburgh)
Harry Pople at the University of Pittsburgh developed CADUCEUS (originally called INTERNIST-1) during the late 1970s and early 1980s, publishing a detailed account in 1982. The system encoded diagnostic knowledge for several hundred internal medicine diseases and was among the most comprehensive medical diagnosis programs of its era.
INTERNIST-I Developed by Jack D. Myers and Harry E. Pople Jr.
Jack D. Myers and Harry E. Pople Jr. at the University of Pittsburgh developed INTERNIST-I, an expert system for internal medicine diagnosis, with its principal public evaluation published in the New England Journal of Medicine in 1982. The system encoded knowledge of roughly 500 diseases and 3,500 symptoms, demonstrating that algorithmic clinical reasoning could approach specialist performance on complex cases.
MYCIN: A Rule-Based Expert System for Infectious Disease Diagnosis, Developed at Stanford University
Beginning around 1972, Edward Shortliffe at Stanford University developed MYCIN, a rule-based expert system written in Lisp that used approximately 600 if-then rules to diagnose bacterial blood infections and recommend antibiotic treatments adjusted for patient body weight, establishing a widely studied model for clinical decision support.