In partnership with Western General Hospital, Edinburgh, the CLRC developed a Bayesian algorithm for the diagnosis of abdominal pain in patients. The CLRC analysed data on 6,387 patients, each suffering abdominal pains described by one or more of 33 identified symptoms. Given this data, the learning machine (a G&T system) outputs a probability that the patient was suffering from each of 9 separate diseases.

As can be seen in the table below, in comparative testing the different versions of the G&T algorithms perform very favourably when compared to human predictions.

Consultants	76%
Registrars	65%
Junior Doctors	61%
G&T	65%
G&T - simple bayes	74%
G&T - CART	64%
Rate of successful diagnosis of trained humans and learning algorithms.