Abstract
Ten simple rules for engaging with artificial intelligence in biomedicine.
Highlights
The first industrial revolution led to mechanical production and steam power; the second, mass production and electrical power; and the third, electronics and computers
There exists a computational “black box,” a phenomenon describing the difficulty of understanding how artificial intelligence (AI) algorithms arrive at a particular result
We propose the following rules to allow biomedical professionals to attain some measure of control and strap down their panic at the sight of words such as “algorithms,” “AI,” “machine learning” and the like
Summary
The first industrial revolution led to mechanical production and steam power; the second, mass production and electrical power; and the third, electronics and computers. “Deep learning” is a specific method to train neural networks, which are based upon different layers of computational “neurons” that recognize patterns (see Rule 3), much like neurons in the brain firing in response to specific visual inputs [7] In learning about these new techniques, biomedical professionals will find that they are already familiar with many of the underlying algorithms. Biomedical experts come to the table with a wealth of knowledge in fields such as genomics and proteomics, bioimaging, medical imaging, brain and body machine interface, and public and medical health management This expertise, combined with the firsthand experience of everyday frustrations and struggles that prevent progress in biomedicine, will facilitate the assimilation of helpful AI models into the healthcare space [3]. The benefits are reaped when these artificially intelligent models help humans live happier, healthier lives
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