lantern-pharma-neural-net-for-cancer-drug-response-prediction-oct-2019.png
Graphic from the FAQ created in response to ZDNet's questions. A neural network built in the R stats program by Lantern Pharma to test drug responsiveness of a combination of 10 different genes. Black lines show the interconnections of artificial neurons as signals from the genes pass through the network from left to right in the "forward pass." Blue lines show the biases applied to the network.  Lantern Pharma.

Artificial intelligence is everywhere in life sciences, and yet, it's hard to know just what it all means. Plenty of headlines tout AI as something that is already making breakthrough diagnoses, an automated diagnostician that is already replacing radiologists. There's a lot of hype, but what can AI really do?

Lantern Pharma[1], a five-year-old, privately held biotech startup based out of twin headquarters in Dallas and Kearney, New Jersey, is taking existing drugs and seeking to secure new use for them as more finely tailored cancer-fighting agents. 

ZDNet interviewed the chief executive, a seasoned entrepreneur named Panna Sharma. To enhance the understanding of AI in medicine, ZDNet subsequently requested additional information in response to specific questions, such as how the company's approach differs from plain old statistics. 

What resulted was an eight-page FAQ, emailed to ZDNet as a PDF file, with details about how machine learning is used to simulate some of the testings that happen in human drug trials. It's an enlightening view of science that goes beyond the headlines. 

It turns out machine learning forms of AI are making inroads in the work of medical diagnostics in small ways, but ways that may make a big contribution at some point down the road. 

"We are not inventing AI, we are leveraging AI, leveraging AI for a

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