Stanford Trains AI To Diagnose Pneumonia Better Than a Radiologist In Just Two Months (qz.com)

A new paper from Stanford University reveals how artificial intelligence algorithms can be quickly trained to diagnose pneumonia better than a radiologist. "Using 100,000 x-ray images released by the National Institutes of Health on Sept. 27, the research published Nov. 14 (without peer review) on the website ArXiv claims its AI can detect pneumonia from x-rays with similar accuracy to four trained radiologists," reports Quartz. From the report: That's not all -- the AI was trained to analyze x-rays for 14 diseases NIH included in the dataset, including fibrosis, hernias, and cell masses. The AI's results for each of the 14 diseases had fewer false positives and false negatives than the benchmark research from the NIH team that was released with the data. The paper includes Google Brain founder Andrew Ng as a co-author, who also served as chief scientist at Baidu and recently founded Deeplearning.ai. He's often been publicly bullish on AI's use in healthcare. These algorithms will undoubtedly get better -- accuracy on the ImageNet challenge rose from 75% to 95% in just five years -- but this research shows the speed at which these systems are built is increasing as well.

Comments

Volume might be the issue

[Bruinwar]

From what I have heard, most radiologists review large stacks of MRIs, CTs, or Xrays. They miss stuff all the time. AI wouldn't get tired, or be in a hurry.

Medicine is too broad a subject to leave to humans

[chaoscustard]

It's becoming increasingly clear that no human can possibly have a functional grasp of all the knowledge required to make accurate diagnosis across all possible conditions. In the current model patients hope that they have something simple or obvious, and if not that their doctor can send them to the correct expert. This has far too many false positives and false negatives built in.

AI systems able to access comprehensive libraries of information are better at this type of work. Sure, I'd want an expert who can tailor search terms, accurately describe symptoms in a consistent manner, but for a number of years now I've been cheering every AI advance in clinical diagnosis. Can't come soon enough.

...in training too.

[DrYak]

Volume also plays a role in training too.

To be considered trained, the radiologist usually have to go through several dozen of hundreds of MRIs, CTs and Xrays.
(They are not litteraly counted one by one. It's just accepted estimation that by the time the medical doctor finishes 5 years intership, he'll have seen enough example to be considered trained enough to have his radiologist certification).

The big advantage of the machine, is that instead of taking 5 years of internship training, you can have the neural net train by going through the 100'000 in one big computational jobs on the cluster.

There's this folk saying (Started by Malcolm Gladwell) that you need 10'000 hours of practice to become a master of anything.
The big benefits of AI is that these 10'000 hours don't need to happen in real-time anymore but can be simulated in a computer.

Old tech and idea

[blahbooboo]

They've had this sort of tech to read digital images for decades. Papnet is used to read pap smears since the mid-1990s.

http://www.lightparty.com/Heal...