The Future of Brain Surgery

Sam DeBrule

Awesome, not awesome.

“Brain surgeons are bringing artificial intelligence and new imaging techniques into the operating room, to diagnose tumors as accurately as pathologists, and much faster, according to a report in the journal Nature Medicine… In addition to speeding up the process, the new technique can also detect some details that traditional methods may miss, like the spread of a tumor along nerve fibers, he said. And unlike the usual method, the new one does not destroy the sample, so the tissue can be used again for further testing..” — Denise Grady, Reporter Learn More from The New York Times >

#Not Awesome
“AI systems will undoubtedly be able to consistently find subtle abnormalities on mammograms, which will lead to more biopsies. This will require pathologists to make judgments on subtler irregularities that may be consistent with cancer under the microscope, but may not represent disease destined to cause symptoms or death. In other words, reliance on pathologists for the ground truth could lead to an increase in cancer overdiagnosis.” — Adewole S. Adamson and H. Gilbert Welch Dermatologist and Researcher Learn More from LA Times >

What we’re reading.

1/ AI systems aren’t guaranteed to be a good thing for health care. If doctors ask the wrong questions, the technology may amplify previous mistakes made in the medical field rather than enable effective interventions. Learn More from WIRED >

2/ Researches work to make the voice tech industry more inclusive, saying that products like Alexa and Siri are falling short because they have not been “programmed to respond adequately to abuse.” Learn More from The Guardian >

3/ A woman, foraging for rare mushrooms in a remote region of western China, changes the fortunes of her family and community with a little help from a machine learning-enabled platform. Learn More from TIME >

4/ Warner Bros strikes turns to machine learning to better predict the likelihood for financial success of a given film before sinking marketing dollars into it. Learn More from The Hollywood Reporter >

5/ The White House roles out new guidelines, encouraging regulators not to stifle innovation. Critics worry that not enough is being done to parse machine learning algorithms for biases before rolling them out to large populations. Learn More from Recode >

6/ Experts studying the spread of “computational propaganda” believe that we are naive to think a purely technological solution will be the answer to the problem. It will take a combination of human labor and AI. Learn More from MIT Technology Review >

7/ In its unending struggle to best Amazon, Walmart places a big bet on robot-automated warehouses to improve their delivery times. Learn More from Bloomberg >

Links from the community.

“Hidden Technical Debt in Machine Learning Systems” submitted by Samiur Rahman (@samiur1204). Learn More from nips >

“Our field isn’t quite “artificial intelligence” — it’s “cognitive automation” submitted by Avi Eisenberger (@aeisenberger). Learn More from Twitter >

“Machine Learning in Production: Serving Up Multiple ML Models at Scale the TensorFlow Serving + Kubernetes + Google Cloud + Microservices Way” by Dr Stephen Odaibo. Learn More from Noteworthy >

“Building OCR and Handwriting Recognition for document images.” by Ajinkya Khalwadekar. Learn More from Noteworthy >

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Read more here: Machine Learnings – Medium

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