It’s been recently published by University of Adelaide a study that focuses on the application of artificial intelligence in diagnostic field, with the aim of determining who, among the subjects involved, has major probabilities of decease in a temporal horizon equal to 5 years (https://www.nature.com/articles/s41598-017-01931-w ).
Far beyond simplistic gruesome consideration one may easily express, the goal of the research is set to be the prevention of non-immediately detectable pathologies that still during the years evolve damaging human body and bringing finally to death.
In this context AI use finds its place, in particular exploiting deep learning algorithms, thanks to which comparisons have been made over a wide database of information regarding patients: not only tomographic images, but also data about clinical history. Analysis coming from new models provided predictions with greater accuracy with respect to radiodiagnostic techniques, showing AI potential in medical diagnostic field.
By the way, in Ab.Acus we support the importance of deep learning implementation in biomedical scope, nevertheless our research works are permeated also by a marked attention to the elders’ life conditions, in the perspective of their continuous improvement and pathologies prevention. The latest example is the European project H2020 MoveCare (http://www.movecare-project.eu/) coordinated by prof. Alberto Borghese from University of Milan, where artificial intelligence is used for monitoring the elders at home.