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Computer Detection Effective In Spotting Cancer
Anti-Globalism notes a large study out of the UK indicating that computer-aided detection can be as effective at spotting breast cancer as two experts reading the x-rays. Mammograms in Britain are routinely checked by two radiologists or technicians, which is thought to be better than a single review (in the US only a single radiologist reads each mammogram). In a randomized study of 31,000 women, researchers found that a single expert aided by a computer does as well as two pairs of eyes. CAD spotted nearly the same number of cancers, 198 out of 227, compared to 199 for the two readers. "In places like the United States, 'Where single reading is standard practice, computer-aided detection has the potential to improve cancer-detection rates to the level achieved by double reading,' the researchers said."
As much as I afree with you, keep in mind that that 199th woman would be really really glad that it was two radiologists and not a radiologist and a computer... her life could well have depended on it.
...you're #199. If the computer provides that much advantage when combined with a single person, it stands to reason that it would also provide a huge advantage when two people read the charts. Unfortunately, knowing our medical system in the U.S., they'll probably just use this as an excuse to pay only one doctor to read the chart....
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It doesn't say if the 198 that CAD found were a subset of the 199 that the two readers found.. So would two readers + CAD have found more than 199? Or did both groups miss the same 28?
http://content.nejm.org/cgi/content/full/NEJMoa0803545
That's the original research. If you read the Yahoo article you'll see the researchers got money from the manufacturer of a computer-aided reading system.
Worry not, this is standard practice. Although there is general support that CAD (computer-assisted diagnosis) is effective vs. a second reader, there is still a bit of controversy in the field from time to time, since the results have not been overwhelmingly in favor of CAD yet. There's always at least one talk on the general usefulness of CAD at conferences. Sometimes whole sections get devoted to the topic.
What is a bit more puzzling is why it isn't as prevalent in diagnosis of other types of cancer. Most of the computer-aided detection algorithms draw on general machine learning and image processing techniques rather than specific domain-knowledge of the breast, and thus many of them can be applied, sometimes without any changes, to other organs. There is nothing particularly special about the breast.
My group developed a CAD system for MRI images of the brain, and in the course of performing experiments to put in the paper, I decided to run a few images from a breast CAD project through the classifier. Sure enough, the classifier we had developed for MRIs correctly classified 96% of the mammograms we fed it as well.
Because the computer systems are expensive and it hasn't been clear that they work as good or better than humans. It's a very complex issue and has been studied for quite some time. In particular, the issue is "false positives" which cause anxiety and often prompt additional, invasive, expensive testing. From a rather quick Google Review of Available Information and Literature (GRAIL):
TFA doesn't even mention the false positive rate, just the fact that it found as many cancers as the double Radiologist method. So keep your pantyhose on. It's something that should get better with time and experience, but it's hard to say that the system is ready for universal application.
Faster! Faster! Faster would be better!
"Saying that computers can be as good as a human at some things is like saying different brands of cow milk taste the same. Why is this not standard now! Computers are more capable at many tasks, especially things that are repetitive and tedious."
And what computers can't do. Cheap labor can.
Shai Schticks:"You don't make peace with friends, you make peace with enemies"
Medical device companies and universities have been working on this problem for years. It just isn't ready for prime-time usage. People go to school for about a decade to become a pathologist, and replicating that kind of domain knowledge isn't an easy task.
If you are a non-programmer, I understand how it seems like a trivial task to identify abnormal cells in tissue. We can naturally recognize similar/dissimilar cells with our vision, but to do this with a computer requires some serious mathematics, namely using a clustering algorithm.
I am actually researching this problem next semester, particularly how well a certain clustering algorithm works when applied to the problem, so I guess thats where I am coming from.
Well, let me tell my tale of working as an assistant in a hospital in Germany.
Medical care is first and foremost bureaucratic, and I guess it's no different in other countries. If it's socialized medicines fault or not is another topic.
The fact is that the processes are horribly inefficient - the computer systems for cancer therapy were from the 1970, and I literally had to hack OpenVMS commands into a terminal with a monocrome green-black display. Then I would have to wait 5 minutes or so to receive ~10mb of data (CT Images).
We had another, "modern" system that should eventually replace the old system, but it was basically the old OpenVMS code with a buggy Win32 GUI glued on. In some aspects, it was even *worse* than the old one. Id would crash randomly, didn't provide shortcuts for the most basic tasks. It was literally so bad that I would not use it for planning my garden - and we were using it for treating cancer patients! Cost of the new system? About 300.000 Euro (415 000 Dollar).
How can this kind of business go on? In my opinion, because it isn't a real business. Money isn't a big deal. The people who made the decision, mostly doctors, aren't really qualified and are easily impressed with what the salesguy tells them. They are in their 50ies and don't really understand this new fancy computerstuff anyways.
I hope this technology, which is based on image recognition (as far as I can tell from the Article), will advance, because it is so obvious and was already obvious to me when I worked there. There is much more to be done in terms of software/engineering in the medical field. I am talking about getting rid of the endless paper trails, not storing images physically in the smelly hangar with the leaky roof, not having to hear doctor say "I can't decipher this.. does this say 'left' or 'right'?"....
/anonymous rant
Parent poster is not offtopic, this troll is expressing its experience with this technology. It's saying that it got a false positive (fp) and underwent chemotherapy as a result, which caused the hair to fall off its genitalia (shaved pussy). Quite a tragic tale.
Trolls are really an amazing species once you learn to understand their language.
nothing particularly special about the breast
Says you.
What, too obvious? Meh. Anywho, I think much of the attention to breast cancer is unwarranted. There are far more common and more dangerous cancers in each sex. I hate to put it this way, but it's fairly easy to isolate breast cancer vs. a brain tumor or liver cancer (mastectomy might not be the favorite choice, but it's pretty easy)
Am I sexist? I don't think so, I just wish that, say, similar attention was being paid to prostate cancer. As far as I know, they're roughly equally prevalent and equally dangerous
I have developed a truly marvelous proof of this comment, which this signature is too narrow to contain.
Most results are presented via ROC curve (for the uninitiated, this is a curve that plots true positive rate against false positive rate based on some threshold for classifying a lesion), so the FPR can theoretically be reduced if you're willing to lose sensitivity as well.
The thing is, the outcomes are not balanced. The risk of missing a cancer is considered far greater than the risk of returning a false positive, so the algorithms are usually created with sensitivity rather than specificity in mind. In my opinion (and since I work on some of these algorithms, my opinion is important :)), this is as it should be, and we should worry about specificity only if we can keep a comparable level of sensitivity.
In any case, the article Yahoo is sourcing from does mention the specificity (which is 1-false positive rate), and it is encouraging: with CAD, the specificity was 96.9%, vs. 97.4% for double reading. Given that sensitivity was also similar (87.2% vs. 87.7%), this article paints CAD in a very favorable light.
Because you have to *PROVE* with clinical certainty (ie. research studies) that the computer system is as good as an expert under all conditions. A mammogram is a two dimensional monochrome picture of a three-dimensional object. As you are attempting to detect a life-threatening defect using a piece of software, false alarms can be as devastating to the patient as missed detections, and thus have the same lawsuit risks.
Also, this requires the entire hospital to have a digital patient record management system, in order to handle digital X-ray images. Many hospitals and dentists are still using photographic plates and paper records. With the digital system, everything from doctors notes to X-rays, CAT and MRI scans are automatically placed into the patients record when they are generated. The resulting data is then accessible to any consultant or doctor involved with the patient. The new system has the advantage that there is no need to wait for X-ray plates to be developed.
Vintage computer adverts: http://www.vintageadbrowser.com/computers-and-software-ads
it boils down to the fact women are better whingers. just as many men get prostate cancer, but more die from it because there aren't the screening services women have.
If you mod me down, I will become more powerful than you can imagine....
what about the one cad missed? i bet that person would be pissed off with you? cad systems still aren't as good as 2 eyes, and when your talking life and death that just doesn't cut it.
If you mod me down, I will become more powerful than you can imagine....
As far as I know, they're roughly equally prevalent and equally dangerous.
No, they are (very) roughly equally prevalent, but not nearly equally dangerous. They typically present very differently, for example there is not a significant population of aggressive cancers in younger people with prostate like there is in mammo.
I fell right into that one, didn't I? :)
I agree. I actually much prefer working with brains; the organs themselves are more interesting and analyzing the images tends to involve more challenges than 2D mammograms. Volumes vs. static images, spatiotemporal analysis, the option of acquiring functional data to map the lesion to cognitive deficits... I find it a very interesting area. Unfortunately, early diagnosis doesn't always make a difference in certain forms of brain cancer. This needs more research in treatment rather than in diagnosis.
Now we're going into the sociological dynamics of research, which turn out to be really messy, but I'm pretty sure the disproportionate amount of interest in breast cancer is in no small part fueled by the ample funding that gets provided to it vs. other types of cancer. However, as I mentioned in the other post, a lot of the CAD methods tend to be general, and breast cancer is really only a specific application, so this is perhaps not as bad as it sounds (if others apply existing methods elsewhere). Given that other forms of cancer strike more often or have greater mortality rates, and that this one tends to strike only half of the population with any frequency (although it is possible for it to develop in men as well), I think something like pancreatic or colon cancer would be more useful to direct some of the study towards, particularly because the current methods for diagnosis are wholly inadequate in the case of pancreatic cancer and rather invasive in the case of colon cancer.
Prostate cancer may also be a useful cancer to study more due to its high prevalence, but it's also gender-specific and the survival rates are rather high already, so I don't think it would be the first cancer to research on my list.
system, there is a synergy between man and machine. Our system was for a general practitioner (general diagnosis with symptoms, physical findings, history, tests, etc as input). The computer is somewhat "dumb", but it always checks all the possibilities. The doctor would be looking for the usual stuff, and sometimes miss the more exotic diseases that would turn up from time to time. The machine would flag some exotic condition with a high probability, and the doctor would go "Interesting! I hadn't thought of that, let's check it out." Dr. House probably doesn't need one :-)
I worked on the first clinically useful mammo CAD system (also the first to have FDA approval in the US). Unlike some of the smaller scale (often academic) programs I saw at the time, there was a large degree of domain knowledge (i.e. breast specific) in our codes.
This is pretty typical in other pattern recognition domains as well. You can get a certain distance with fairly generic approach algorithmics, but to really push the performance boundaries, you need local info and approaches as well.
This paper doesn't actually say anything particularly new relative to our results 10 years ago, but it's a broader study than was available then.
Clustering algorithms are generally unsupervised. The domain knowledge isn't usually necessary in clustering so much as verifying that the cluster results make sense, since they're much more difficult to quantify than supervised tasks, such as classification, where your data is already labeled.
Of course, you need to do that too before you can present meaningful results and convince people that a system works.
(*As an aside: although you can certainly use a clustering algorithm to segment, the problem you've identified is technically lesion segmentation rather than clustering. There are a lot of non clustering-based approaches to it as well.)
Yeah, but can they please choose another acronym? Computer Aided Design has been around a very, very long time. How about Computer Enabled (or Enhanced) Detection (CED) or Computer Facilitated Detection (CFD)?
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Be careful: A slight improvement in the classifier (or acceptance of another false positive or two) and you may have to make that argument in the other direction. The difference is accuracy is not statistically significant for a binary classification problem of that size.
What this article demonstrates is that current state-of-the-art CAD is nearly as good as a second reader. The performance of the radiologist is pretty much fixed; the algorithm's performance is not.
Some people like to call it Computer Aided Risk Estimation (CARE), although some also use this term as a subfield of CAD, but unfortunately, the terminology has become entrenched by this time.
Because mammography is an extremely non-sensitive test.
http://mammography.ucsf.edu/inform/html/graphics/graph2a5.gif
This shows how few women the test can actually benefit - 37 out of 10,000 over all lifetimes. Even worse is that the women who are diagnosed falsely positive far outstrips those that actually have cancer by orders of magnitude. This creates a harmful burden on the falsely diagnosed women - creating morbidity and even mortality.
You can make a machine that gets 99.9% of all women who do have breast cancer. Unfortunately, out of the 9740 that never will/don't have breast cancer 9700 will be falsely diagnosed as having it.
Why? Just because something was one way when humans were doing it doesn't mean an intelligent system over time will become attuned to variables we do not even understand let alone know how to properly implement in an algorithm. I think we can do better than you say.
An Education is the Font of All Liberty
As a med student, I couldn't be more pleased about this. Hopefully by the time I get out there, they'll have these standard in hospitals. And, more importantly, part of the standard of care, so when they screw up, I wont be sued.
Have you ever tried to see a small diffuse tumor on an X-ray before? It take a Jedi mind trick on just to convince yourself they're there.
X-rays are cheap, fast, and awesome for bones/opaque liquids, but my eyeballs can't see loose tissue worth a crap.
Latewire
that's a statistically insignificant difference in accuracy. i think the conclusion to be drawn from this is that computer-aided detection is much more effective than an unaided human expert. this has significant implications when doing cost-benefit analysis.
the cost of an extra computer is likely a lot less than another technician or radiologist. so this data will help medical institutions make better use of funds while improving quality of patient care. it doesn't mean they have to lay off their radiologists/technicians and replace them with computers, but perhaps they could add a computer to their radiology lab and allocate new personnel for other tasks that demand human judgment.
There is nothing particularly special about the breast.
3 billion men beg to differ.
Even those who arrange and design shrubberies are under considerable economic stress at this period in history.
"Computer Detection Effective In Spotting Cancer"
I detect computers in my room, thus that means there is cancer in my room? Ick.
*"Volumes vs. 2D images", that is.
Where do you get your stats from? I've seen otherwise (ACS site, for starters. School, too, in my community health class. I'm in nursing). Furthermore, of all the inequities in research and healthcare, this is just one that is female-positive. Take, for example, cardiovascular health and women. Women are treated differently when it comes to suspected heart attacks and other issues of cardiovascular health, and it usually winds up killing them.
Because government-funded research is inherently free of any and all bias. It is never politically motivated, and areas to research and not to research are chosen purely on scientific merit by a government bureaucrat, whose #1 goal is not to increase and extend his own power. </sarcasm>
Seriously though, there are a lot of people who believe exactly that, and even if the commercial research may be biased, at least that's known and out in the open.
Why is this news and NOT standard practice already?
Actually it is reasonably widely used as a diagnostic aid and becoming more so all the time, at least in the US. I've personally done consulting work in radiology clinics where they use computers to assist diagnosis. That said, it is still a developing technology and every scan is read by a radiologist too (which is just common sense) but these system do occasionally catch something the radiologist missed and vice-versa. It provides another set of eyes which don't get tired and that is a useful thing.
Why isn't it more widely used? Several reasons. First while impressive, these systems are still being tested for clinical utility. So far are not demonstrably better (meaning they don't catch more tumors under double blind testing) than a radiologist. But achieving statistical parity is an impressive feat and worthy of mention. Second, these systems are not cheap and unfortunately yes that matters. There is not an infinite amount of money available for healthcare and in many settings the computer system cannot be justified unless it is demonstrably and significantly improves diagnostic capability. I have no doubt this will be standard equipment in time but it will not happen overnight.
Computer Aided Diagnosis has been around for years - it's just now it's becoming more popular.
Don't get me wrong - this is great stuff, just not new.
Depends... what's its false positive and false negative rate for Lupus?
That's the presumption, but it's often false. An algorithm can often outperform an expert's intuition. See the chapter on "Evidence Based Medicine" in the book "Super Crunchers," or the chapter on diagnosing heart attack in Malcolm Gladwell's Blink. Often in these classification tasks, an expert using a statistical tool performs worse than the statistical tool alone, because they override it and degrade its performance. The human mind is very poor at properly weighting a large number of factors and considering all their interactions.
Because it's not nearly that straightforward. Reliable image recognition in a clinical setting is tricky. Mammograms are a good place to start because they're fairly well controlled. There are rarely any serious artefacts or patient motion. Breast tumors are fairly obvious, and the cost of false positives isn't very high (a biopsy, which is a bit painful but is a simple outpatient procedure done under local anesthetic).
Much of the stuff radiologists do is a LOT harder. Even with the easy stuff, computers are JUST starting to get good enough that we might start thinking about trusting people's lives to them.
Aim higher. You can easily make a machine that flags 100% of the women who have breast cancer. Unfortunately it also gives the nod to 100% of the ones who don't.
In other news, mass firings across the nation as radiologists caught e-mailing photos of topless women around, about 31,000 photos in all.
Modding me -1 troll doesn't make me wrong.
i built the original software which was deployed by the NHS around 1998. The systems now are several generations ahead. Both groups would find different 28s with large amounts of overlap based on the smaller studies we did. Unfortunately mine was torpedoed due to liability issues the first time around. Mayber theyve figured out a way around the liabilit caused by the computer missing a tumor but probably not.
BTW, mine was open source http://yhs.sf.net for your code viewing cancer analyzing pleasure.
In all seriousness, how else do you assign a weight between two vertices without domain knowledge?
Seems to be that the graph shows that 50 too late to start getting mammograms. From what I understand, it is recommended that women start getting mammograms at 35.
Also, isn't the point of the mammogram to detect anomalies before they turn into cancer? The numbers for whatever reason, seem a bit skewed in an attempt to get the most disproportionate ratio possible.
you still need a weight function to determine how similar two vertices are. This is where domain knowledge comes into play. A clustering algorithm without a measure of similarity between points is useless.
Yeah, but how well does it do against 4 experts from India? Hardware and software required to email images will hardly cost as much as this image recognition setup.
Politicus
Are you referring to the distance metric used in clustering algorithms? Most simply use Euclidean or cosine distance unless they have specific reason to believe a different metric would work better. It isn't really domain dependent.
Or did you mean how "else could you segment a lesion other than by clustering it in the absence of domain knowledge?" There are numerous ways, but edge detection filtering, fuzzy-connectedness segmentation, and watershed segmentation are the first ones that come to mind. They're all general image processing tools.
That's not to say that you couldn't create a clustering algorithm out of those, or that you couldn't combine domain knowledge with these algorithms (the more you know, the better your model can be). But you'd probably get better performance from dedicated clustering algorithms, such as k-means, over using segmentation algorithms in clustering... and perhaps vice versa.
Well, hopefully imaging can be augmented with this:
http://www.boston.com/news/globe/health_science/articles/2004/09/23/hope_seen_for_early_test_to_detect_breast_cancer/
By Robert Cooke, Globe Correspondent | September 23, 2004
Harvard researchers at Children's Hospital Boston have developed a simple urine test that appears to detect breast cancer early and accurately track tumor growth.
The findings are still preliminary, but if further research supports them, the test could be a major advance in the effort to catch breast cancer before it turns deadly. The Boston scientists are searching for similar markers in urine for other cancers.
You must have test your algorithm on some pretty easy breast cancer cases and/or tested on a pretty small sample. They are reporting 87.6% accuracy using a human and their CAD system, while you are getting 96% using just a computer designed for a different type of cancer. Something doesn't add up.
Chris Mesterharm
>In a randomized study of 31,000 women, researchers found that a single expert aided by a computer does as well as two pairs of eyes.
"As well as"?
> CAD spotted nearly
or "nearly as well as"?
> the same number of cancers, 198 out of 227, compared to 199 for the two readers.
Ah, 198 vs 199 - it seems their first statement is not accurate. I wonder why people keep doing this - they use numbers accurately enough, but use language inaccurately.
Max.
From my limited understanding, the algorithm is a dedicated clustering algorithm (it actually incorporates k-means), but it also utilizes a graph-cut algorithm for segmentation.
I personally think that when you're dealing with something like cancer, even if the computer-assisted detection is ALMOST as good as two humans, it's still not good enough to be used on a regular basis.
It's all well and good to say that it's almost as good as two humans together, but I'm sure the couple of dozens of people who slip through the cracks would have something to say to the contrary.
I mean, imagine if you had two bullet-proof vests -- one with multiple layers that let bullets through 23 out of 10,000 times, and one with a lightweight, high-tech material that let bullets through 89 out of 10,000 times. Would you really want to go with the latter?
http://www.tenjou.net/
Yes, we're still investigating why the accuracy we obtained was so high. We've ruled out overfitting. I suspect it may be because our images are galactograms (i.e., mammograms with contrast injected into the ducts prior to imaging), which can more easily visualize certain types of abnormalities than unenhanced mammograms. I believe they also come from a single scanner, which isn't usually the case in clinical studies (although I wouldn't expect this to have as much impact in mammography as it does in modalities such as MRI).
Our dataset is smaller than the one used here, but not so small: we had 54 images, 13 of which had tumors. We're correcting for the balance of the classes both by iterated random sampling and by ROC analysis, however, so I'm pretty sure that's not a factor.
Interesting comment in that article:
"In a control population of 46 women without cancer, there were seven false positive results, or 15 percent. In these seven women, the amounts of the telltale enzyme were very low, the researchers said."
Is it possible that they are able to detect the cancer even sooner than they realize? How can they be 100% sure that all 46 of these women did not have breast cancer? If we can only be 87% accurate...
I've got a preventative treatment for prostrate cancer.
It's called trans-fatty acids.
Take enough of it and your odds of getting prostrate cancer go way down.
There's plenty of scientific evidence to back my claims.
The future of breast cancer detection is Gamma imaging. See http://www.dilon.com/bsgi.php for comparisons between BSGI and X-Ray.
In Newfoundland, Canada, a lab that screwed up testing thousands of biopsies as false negatives has been calling the women to inform them they should get re-tested, only to be told that they have died . . . from cancer their lab tests said they didn't have. I'm sure women who were really negative would have had no problem dealing with the stress of retesting if it meant Grandma/Mom/Sister/Daughter/Girl-Friend would be alive today.
"All those, moments will be lost, in time, like tears, in rain. Time to die." Roy Batty
I mean, somethin like this?
One frequently recounted tale is that the first computer system to systematically beat most doctors in diagnosis (in an admittedly narrow domain), the blood-infection diagnosis system MYCIN from Stanford in the early 1970s, was never deployed due in significant part to opposition in principle to having computers do diagnosis. (Another major reason, of course, was that computers at the time were expensive, clunky to interface with, and not already routinely installed at hospitals.)
I suppose the situation may have improved over the past few decades? I research in AI myself (though not bioinformatics-oriented AI), and I'd say probably most people in the field still assume that the medical profession is a bunch of anti-AI luddites, possibly driven by self-interested doctors' organizations who don't like the idea of being "replaced by machines".
10 PRINT CHR$(205.5+RND(1)); : GOTO 10
Anybody see what the detection rate is with only one MD looking at the images? The article seems to be missing that bit of date. I'm willing to bet it is it 'statically' lower than the two MD system.
This would be proof that four eyes are better than two! =8-]
"All those, moments will be lost, in time, like tears, in rain. Time to die." Roy Batty
'AutoBREAST'... But, i also instantly thought of Star Trek's medical tricorder, too. (captcha: defects)
Previously: "Linux... Toward the Sunrise..." Now: "Linux... Toward the-- No, now, part of Every Sunrise"
Hardware and software required to email images will hardly cost as much as this image recognition setup.
Guaranteeing privacy will be much more complicated.
The channel between the US radiology machines and the Indian doctor's office has to be protected against unwanted intruders. Email doesn't cut it as it is as secure as a post card.
Sadly, slapping a GPG plugin to thunderbird won't do the trick either because :
- I'm pretty sure legislation are rather complex and will require solution that have been explicitly approved and certified (someone need to get the FDA and similar organisations in other countries to approve the encryption plugin - and that will cost money)
- Depending of the data source the DICOM files (medical imaging standard format) can get pretty huge and beyond the tiny limitation of e-mail attachment size (mammograms are very high resolution and that's only 2D, other type of image are 3D and can weight in the order of gigabytes).
This will probably require establishing encrypted VPNs.
- Probably using a small set of approved VPN boxes.
- and involving an administrative nightmare at both ends to obtain proper clearance. (probably this will end up being the US hospital setting a separate PACS server isolated from the rest of the network for security reasons).
And then there's the question of the compliance of the equipment used by the Indian doctors (at least the open source OsiriX for Mac OS X is currently getting FDA-approved)
And that's only the technical part. Then there's the whole question of legal liability complexified by the fact that the doctors who did the diagnose aren't even living under the same juridiction as the rest (hospital, patients and so one).
Whereas, with the CAD software, the situation is much simpler.
- Only the problem of liability comes.
- And as the software is operated by a doctor, you can even bypass the liability by saying that the final diagnostic decision is the doctor's, so he's liable for whatever is decided, the software is only a helping tool (After all it's called Computer *Assisted* Diagnosis).
---
Last but not least, there's the problem of different standard in medical education and training. The Indian doctor's knowledge may not be approved by US.
Indian doctors learn standard scientific medicine. :-P
Not intelligently-designed creationist bible-compliant medicine ~
"Sufficiently advanced satire is indistinguishable from reality." - [Tips: 1DrYakQDKCQ6y52z6QbnkxHXAocMZJE61o ]
software is already used for similar activities, but in another way. With medical images, it is often difficult to decide that something is not there. It is more easy to see e.g. a tumor than to decide that no tumor, even not a small one, is present. So, in some laboratories, they use a computer to judge the images first. The computer will only flag the cases where he found a tumor and also indicates the place. As such the doctor only has to do a quick verification: he knows where to look and what to look for, because the computer has given him the required information. As such, the medical expert has more time to judge the difficult cases in more detail: all cases where the computer did not find anything. Tests have shown that with that system, fewer experts can process more images with the same level of accuracy.