Machine-learning systems diagnostic capabilities are widely used in medicine. There are algorithms able to predict if a person at risk of developing psychosis caused by schizophrenia will develop the condition with 100 percent accuracy by analyzing his or her speech, which can exhibit telltale signs of the condition.
An automated microscope uses an artificial neural network to rapidly analyze blood samples in the field and diagnose malaria with 90 percent accuracy. Traditional rapid diagnostic methods can only determine if the malaria parasite is present in a blood sample, which does not necessarily mean a person will contract the disease.
A surgical robot called the Smart Tissue Autonomous Robot (STAR) can administer stitches more precisely than human surgeons. STAR analyzes data from specialized 3D and infrared cameras in real time to generate a plan for an optimal arrangement of stiches and administers them with a robotic arm. In tests, STAR’s stiches were more consistent and resistant to leaks than human surgeon-administered ones.