I am a radiologist and researcher predominately focused on AI.
You could also look at the market, one of the biggest players, Paige, was acquired for about 30% of the money they raised.
Do you think this will result in more routine/boring/tedious tests? Is the bottleneck on these things the human time to analyse them?
For some things, like 3D volume segmentation of structure or disease (e.g. CVA/stroke volume, cardiac muscle mass, iron quantification) the bottleneck is the time it takes so we currently use approximations like single longest dimension, circular regions of interest, etc. AI will dramatically increase accuracy allowing for more accurate treatment and easier large scale research with quantitative endpoints.
Other things people think of like detection of aneurysms, fracture, lung nodules are not “hard” but AI has already added and will continue to add the second-reader benefit which will reduce detection errors. For this category the clinical benefit is as of yet unclear and we know that increased detection does not necessarily translate into improved patient outcomes and can in fact make them worse from over-diagnosis which means investigation related harms and over-treatment.
We were already in a phase of “over detection” in much of radiology with advances in imaging technology so the incremental benefit of current AI remains to be seen and I personally think is going to be much more limited. I had a case recently where a 2 mm brain aneurysm was missed on 3 CT scans over 10 years but was picked up by AI so now is being followed annually. This is too small to treat considering the risks and a serious argument could be made that 10 years of stability is proof enough that this is almost certainly clinically irrelevant for this patient.
Far more interesting areas of AI in imaging are in acquisition of acceleration (i.e. the medical equivalent of upscaling) which can dramatically decrease costs and increase accessibility as well as analyzing imperceptible features.
It may not be a popular take here but in my opinion the future of radiology is like what we see in software engineering today - a skilled human equipped with AI will outperform humans without AI and AI without humans, the latter of which we are still several years away from prototyping due to various technical hurdles.
> in my opinion the future of radiology is like what we see in software engineering today - a skilled human equipped with AI will outperform humans without AI and AI without humans
I suspect this will be the case across the board. It's a useful tool, but it's just a tool. It's not a replacement.
They will never beat the human instinct tho, but they can be great tools sometimes. Unfortunately, LLMs mostly produce garbage.
Essentially the cutting edge reaches up to 99% of human performance on the task it is trained, which is good enough for triage but not for a final diagnosis. However, magic sometimes happens when you train a model to detect something, which you already know is there, on an examination that is cheaper, faster or less invasive than the human"gold standard". Conveniently, this dataset exists since it's common to first do a cheap examination like an X-ray, and then escalate if nothing is found (or if something is found that you want to see better, or a number of other possibilities).
Examples of AI outperforming humans like this includes AI detecting sacral fractures on x-rays better than radiologists (who normally take a CT to conclusively exclude it), detecting potential precursors to pancreatic cancer on non-contrast CTs (where contrast or an MRI is usually required) and detecting an occluded coronary artery on an ECG without the archetypical "ST-elevation changes".
See the link below for references: https://pmc.ncbi.nlm.nih.gov/articles/PMC9478257/ https://www.nature.com/articles/s41591-023-02640-w https://rebelem.com/a-winning-hand-in-cardiology/
So AI, as a general rule, doesn't usually match or exceed the upper bound of the "gold standard" medical performance. But it tends to carry the quality of the upper bound downwards towards the faster, less expensive and invasive methods. In some cases, like in the case of EKGs, that's huge. In some cases it saves time, in some cases it decreases miss rates from tired radiologists or triages their review feed. And in some cases it's not very useful.
LLMs doesn't come close to specialized radiology models at the moment, because LLMs are more about applying knowledge than creating new correlations. Of course that's also hugely useful, but that's a bit of a different topic to unpack.
A self driving car doing better than a drunk on the freeway doesn't reassure me that it'll do better than sober me in a snowstorm.
A whole lot of doctors, if not most, didn’t pick their profession out of an interest in medicine…