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A rather long read to establish the idea that AIs aren't very good at handling types of data that were not in their training set.

AI isn't good at any problem in which it's required to show its working. That lack of accountability is a critical deficit in many of the applications for which it is proposed - health and justice being prime examples.

It always apoears to me that the most enthusiasm for AI comes from those people who are developing and selling it, the same as with anything else.

Various forms of machine learning obviously have the potential to be of extreme value in medicine and everywhere else but these systems are only as good as the people who wrote the code and developed the teaching system.

One of the biggest problems is that people tend to relax and let the gadget do all the work when really it should only be an aid, relying on what is essentially new and unproven tech that needs years if not decades more development should not be done where health and life are st risk.

Years ago it was found that some photo copiers tried to enhance images, and in the process sometimes changed letters and digits in copied images. So they looked at a rather low quality digit 8, decided it was a rather low quality digit 3, and replaced it with a nice looking 3. It seems the same thing happens again.

They are using AI systems to alter images? AI is barely capable of recognising images (actually isn’t...), and some idiots are proposing using AI to “touch up” images that peoples’ lives depend on? Adding or removing details at the whim of an algorithm that is not transparent in its operation. Those bastards really only care about the money...

I made the same point about an all-singing-and-dancing medical document system I once worked on: if you provide the ability to change documents already within the system, you have effectively created a medical malpractice mechanism. It doesn't matter how much A or I is involved: the original data must be clearly accessible and any subsequent amendments obviously signposted.

It's actually a very active area of research. One large application area is site harmonisation; different scanners and manufacturers produce images that look different. To make analysis easier (my area is research, but people also want to use this stuff for diagnostics) you'd want them to look the same. It's quite possible to train something like a generative adversarial model to produce synthetic images that effectively have the scanner variations regressed out while leaving the image detail. People also try to do this for imputation of missing data or remove artefacts from images (such as motion blur and ringing in MRI). Classical techniques like deconvolution can do the same thing in theory, and methods like deep neural nets effectively approximate arbitrary functions, so why not?

A lot of work goes into validation on unseen data sets, however I do usually remain a bit sceptical. I think people often forget you can't put information back into images that has been lost or washed out by noise. You're basically smearing uncertainty around at that point, and possibly into modes that don't look like uncertainty.

"They are using AI systems to alter images? AI is barely capable of recognising images (actually isn’t...), and some idiots are proposing using AI to “touch up” images that peoples’ lives depend on?"

Doing it in a healthcare setting seems particularly stupid, but the whole idea is just bizarre no matter where it's used. The plan is to take images that need to be analysed for any small variations or anomalies, and first put them through software that will edit out any small variations or anomlies. It's just nuts. There's no such thing as "enhancing" an image. The only things you can do are either remove information that is already in it, or add extra information that is not in it. That's fine if all you want to do is mess around with things to make it subjectively more pleasing to the human eye, but it can only ever be actively harmful when it's the raw information content of the image that is of interest.

Machine learning and image recognition may well have a place in the world, although for the most part they're not really ready for prime time just yet. But the idea of using machine learning to edit images that are then passed on to a completely different system (whether human or otherwise) to actually anaylse is just utterly insane.

In the same way that CAPCHA uses difficult images with extra lines over disordered / incomplete letters (similar to photocopier example above)

or searching images for fire extinguishers will find a UK postbox with a shovel leaning on it and decide its also an extinguisher.

Back in real world where image quality is another variable, never mind the subjects in the images, fog, focus, object movement, changes in materials etc... It is hard for me to imagine AI keeping up with all the variables, never mind having a quality interpretation for both detecting and interpreting them.

I also have a concern that if AI did indeed get good enough, we effectively stop training the medical staff that teach and validate the AI results. This will create a new negative feedback loop. Particularly, as again noted above, the system cannot even show its working out either to train people, or to correct its results.

AI has masses of potential, but in my opinion we are looking for quick wins when there shouldn't be one....

Anon for obv reasons

Ages ago I worked on system for analysing images taken from slides of potentially cancerous cells - algorithms gave numeric output as estimate of whether it might be cancerous.

We developed a whole lot of algorithms "manually" (configurable front end to various parameters used) for different tissue types and stains used.

Algorithms developed in consultation with the medics as they were explaining what the key features were in the diagnosis for the images.

Working with the medics we would tweak algorithms and parameters to get a system that gave "risk" outputs similar to what the medics would give themselves.

This system was used, not to replace medics, but to aid them.

For a given slide image medics would see the "risk" rating given by our system, and if their initial rating was low risk but algorithm showed high risk, they would use this as spur to re-examine in case they had missed something.

It worked well (I'm assuming still in use, left that role) in quite a few cases medics would change their risk estimate based on a "second look" prompted by the algorithm, or consult a colleague for a second opinion, (humans not perfect, get tired, and crucially because slide can have a lot of cells and human altering magnification , deciding on which areas to look at more closely, easier for something to be missed than computer which "looks" at all areas of the slide equally

Diagnosis from medical images is not an exact science, but can be helped. This non AI method, developing algorithms based on the features the histologists would look for themselves, might have taken longer but did have advantage of knowing why it gave the results it did, compared to "black box" of an AI. It also meant once key sets of algorithms developed, easy to create new ones for different tissue / stain combinations.

It's accuracy was generally better than skilled medics (did struggle on the occasional real outlier slide, but to be fair so did the medics, but not quite as much ) but should emphases medics still did ALL the diagnosis, this system was just an aid (and a very useful one)