Despite all of its promise (and hype), deep learning has yet to revolutionize biomedicine or definitively resolve any of the most pressing challenges in the field, according to an extensive review article published today in the Journal of the Royal Society Interface.
Bioinformatics professors Anthony Gitter and Casey Greene of the Morgridge Institute for Research, set out in summer 2016 to write a paper about biomedical applications for deep learning. They wanted to see where deep learning was making a difference and where the untapped potential lies in the biomedical world. Their endeavor triggered an avalanche of academic crowdsourcing, with more than 40 online collaborators contributing enough to become co-authors.
Deep learning is part of a broader family of machine learning tools that has made breakthrough gains in recent years. It uses the structure of neural networks to feed inputs into multiple layers to train the algorithm. It can build ways to identify and describe recurring features in data, while also being able to predict some outputs.
Greene says deep learning has not yet revealed the "hidden cats" in healthcare data, but there are some promising developments. Several studies are using deep learning to better categorize breast cancer patients by disease subtype and most beneficial treatment option. Another program is training deep learning on huge natural image databases to be able to diagnose diabetic retinopathy and melanoma.
Deep learning also is contributing to better clinical decision-making, improving the success rates of clinical trials, and tools that can better predict the toxicity of new drug candidates.
"Deep learning tries to integrate things and make predictions about who might be at risk to develop certain diseases, and how we can try to circumvent them early on," Gitter says. "We could identify who needs more screening or testing. We could do this in a preventative, forward thinking manner.
According to Giitter, the team took a software engineering approach to writing the paper, using the GitHub website, which is the most popular place online for people to collaborate on writing code, as the primary writing platform. The participating authors frequently provided examples of how deep learning impacted their corner of science. For example, Gitter says one scientist contributed a section on cryo-electron microscopy. Others rewrote portions to make it more accessible to non-biologists or provided ethical background on medical data privacy.