Earlier this year we highlighted how machine learning, artificial intelligence and big data might have an earlier than expected impact on clinical decision making. Quite a few sceptics scoffed at this idea.
Since then we have seen some nifty examples emerge at various conferences relating to clinical analyses such as this one at ASCO, although there have been quite a few others.
This latest post isn’t about deep learning per se though, but rather how can we look at the tumour microenvironment differently in ways which might help us make better or earlier clinical decisions?
There are a quite a few high profile examples where the emerging research is starting to look helpful so it’s time to link all the loose ends and take a thoughtful look at what we can learn from a particular example involving a high profile study.
The results, some of which intuitively make sense and others are surprising, may give us some useful clues of where to start looking next in terms future therapeutic interventions…