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

Much more complex

[DrYak]

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.

No, it's much more subtle than that.

The things that they teach you at med school, are mostly rule of thumb. Simple algorithms that you can learn so you can get through your job without killing too many patients.
To diagnose pneumonia, there's a check list of things that you learn to look for and which allow you to say with some relative certainty whether or not the thing you're seeing is pneumonia.

Then there's the "clinical" experience. After seeing things for thousands of times, you start recognizing them automatically.
Like looking at an X-Ray picture and immediately "feeling" that there's "something funny" without even needing to start going through any checklist.
It's your old family doctor who can automatically guess the problem just be looking at how you walk like entering his office, or just based on the noises he hears outside, from the waiting room.
(Of course, there's some part of Sherlockian lightning fast thinking and deducting going on and a strong focus on very small otherwise imperceptible details.
But there's also some part of instinctive almost-sub-conscious gut feeling - how else would you know on *which* of the thousands of small imperceptible details to focus your inner Sherlock on ?)

That not something that you can learn by rote memorization during medical studies. That's something that comes slowly over time with practice.

The big advantage of neural nets, is that you can simulate all this experience inside a computer, by "simply" throwing hundreds of thousands of pictures at the neural into in a huge computational batches on the cluster.

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 the end, as any other advances in the artificial intelligence field, you'll still need human oversight in the foreseeable future.
AI currently isn't replacing the job of actual doctors, as it is in providing more information faster to help taking the decision while taking all other informations on the way.
(Just like ECG able to propose diagnostic didn't cause the cardiologist specialist to disappear over-night).

Yes let the AI decide.

[DumbSwede]

I had unnecessary lung surgery 12 years ago because I was misdiagnosed with lung-cancer when all I had a mild fungal infection common in my area at the time called histoplasmosis. I’ve no doubt AI’s would prevent these kinds of mistakes. I also remember being told my chance of having lung cancer was 97% by the radiologist. I’m pretty sure as a young non-smoker he didn’t factor in any kind of bayesian statistics to arrive at this answer, but just looked a chart based on signal return from a PET scan. Hopefully these days at a minimum there would be some app that has them plug in relevant variables and does the bayesian analysis for them.

Trust me, recovery from invasive lung surgery is no picnic.

We don't get bent out of shape when machines read bar codes to price our grocery items instead of a human reading the price sticker. The machine is doing essentially the same thing, but far more accurately. The only difference is we take the price and refactor into a visual form easier for the machine to read. Imagine the accuracy if the Human had to read the bar code instead and remember the item associated with it.