Artificial Intelligence (AI) predicts humans lifespans

Now Artificial Intelligence predicts the human’s lifespan simply by looking at images of their organs, says a new research led by the University of Adelaide.

Researchers from the University’s School of Public Health and School of Computer Science, along with Australian and international collaborators, used artificial intelligence to analyze the medical imaging of 48 humans chests. This computer-based analysis was able to predict which person would die within five years, with 69% accuracy — comparable to ‘manual’ predictions by clinicians.

This is the first study of its kind using medical images and artificial intelligence.
“The accurate assessment of biological age and the prediction of a person’s lifespan has so far been limited by doctors’ inability to look inside the body and measure the health of each organ.
“Predicting the future of any patient is very useful because it may enable doctors to tailor treatments to the individual,” says lead author Dr. Luke Oakden-Rayner, a radiologist and Ph.D. student with the University of Adelaide’s School of Public Health.

“Our research has investigated the use of ‘deep learning’, a technique where computer systems can learn how to understand and analyze images of human organs.

While the researchers could not identify exactly what the computer system was seeing in the images to make its predictions, the most confident predictions were made for patients with severe chronic diseases such as emphysema and congestive heart failure.


“Instead of focusing on diagnosing diseases, the automated systems can predict medical outcomes in a way that doctors are not trained to do, by incorporating large volumes of data and detecting subtle patterns, Our research opens new avenues for the application of artificial intelligence technology in medical image analysis, and could offer new hope for the early detection of serious illness, requiring specific medical interventions and the lifespan of an individual. Although for this study we use a small sample, our research suggests that the computer has learnt to recognize the complex imaging appearances of diseases, something that requires extensive training for human experts.” – Dr. Oakden-Rayner says