They are mistaken. I am a practicing radiologist in the US. We regularly work-up findings from private pay whole body screening MRIs and the workups are covered by insurance.
1,949 karma · joined March 5, 2013
> Ultimately, it should be about personal freedom. This is not a contagious disease we're dealing with here.
The state regulates health and medical devices for a reason. Look at what’s happening with all of these prescription mill apps these days which are still theoretically overseen by a licensed healthcare professional - plenty of medical errors and harm in the name of increasing access (a good thing, when done well). We’ll see criminal investigations within a year.
2. Doctors who depend on revenue from forced visits to renew prescriptions are grifters and not close to a majority. The profession obviously isn’t perfect but the vast majority of physicians I know would be happy to lighten their rosters.
With that said it can’t be a system that creates problems and dumps them back on physicians to fix, hence #1
That’s the goal for doctors too. It would be great to get simple things off the system.
But I think the more realistic intermediate step is a trained person cheaper than a doctor - nurse, PA, etc - aided by AI. The current generation of agentic AI doesn’t seem to be there yet and is too agreeable from RL.
“you’re probably fine sleep it off combined with: drink more water, eat healthier, exercise more, sleep better, consume less alcohol and quit smoking/vaping” +/- “we’ll check some labs to make sure” is the correct answer for probably 95%+ of encounters so it’s not hard for an automated system to handle most simple things, even without AI.
The only takeaway here is logging helps detect patterns, we already knew that.
Radiologist as well. Remember this is full wave inversion not pulsed wave B mode. You can get much more useful information from both high low frequency and capture transmitted waves.
There is promise with this and we use it for example with MRgFUS. With advanced computational models or patient specific CT/ZTE MR aberration correction it is theoretically very feasible to image the brain with ultrasound, whether that’s more useful than say portable low field strength MR is a different question altogether.
> This is cool, but ultrasound is not CT.
Not to be pedantic but since this is a tech forum I would clarify that FWI US is computed tomography by definition (at least in this and many applications). Gas degrades conventional CT too, it’s just worse with US as you have little to no forward propagation and of course innumerable interfaces in the lungs to reflect and scatter.
It’s already used in breast imaging (SoftVue) and hasn’t replace mammography. A body part ideally suited for ultrasound.
More compute many minimize some of the fundamental limits of sound waves (bone and gas) but I would be shocked if they have useful images of 90% of the body parts we image with CT or MRI and even beyond that I question how much it’s more useful than B-mode anyway.
Quite slow which means most things abdomen and chest will be motion degraded.
This may be useful in superficial areas but then why do whole body anyway. Might be some new niches and interesting research but hardly revolutionary in my opinion.
A good primer: https://pmc.ncbi.nlm.nih.gov/articles/PMC2477686/
DNA is increasingly used in oncology, but is difficult to interpret elsewhere and in many tumors is not insightful.
> The data to make the correct diagnosis is out there, we just don't have the tools or processing power to use it yet.
Maybe, but we don’t know what or how to measure it.
> If adrenal nodules of similar diameter behave differently, then the tests will inspect more than just diameter.
Everything investigated so far such as: biopsies with histology, MR spectroscopy and measuring the diffusivity of water molecules has not been reliable in differentiating benign or malignant nodules so we still use size. These are nontrivial problems. There are technical limitations to our measuring tools.
“Normal ranges” in lab values are just confidence intervals of population means which by definition that some normal people will have abnormal values and some patients with a disease will have normal values.
The same is true for imaging. For example we use size criteria a lot. There is nothing different about 4.1 cm adrenal nodules and 3.9 cm nodules to explain why the former gets surgery and the latter gets called benign other than pre-test probability and acceptable false positive and false negative rates, whether this is measured by a human or AI.
This is a loaded UK-centric policy/humanities term and I would suggest using sex/gender disparities instead which does not imply animus and is therefore much more useful for productive discussion.
Implicit and systemic biases in medicine are very real and supported by ample data.
> Systemic in particular is that basically the vast amount of knowledge amassed in the medical sciences has come from studying men. Comparatively little for those not assigned male at birth.
At least for the US this hasn’t been the case in clinical research for the past 15 years or so which in aggregate leans a bit more female than male if anything. Some specific fields still have sex disparity in clinical research for a variety of reasons but that’s the minority these days.
Anecdotally, my practice has most FDA approved AI deployed as we are an evaluation site and very rarely is the AI result useful. Over the past few months we have been cancelling contracts as these cost quite a lot of money (in some cases eating >50% of the study interpretation cost) for little to no benefit and a LOT of noise.
In real life pathology is a spectrum not a binary and physicians are not trained to be 100% accurate instead optimizing sensitivity and specificity considering pretest probability as well as the harms of overdiagnosis and under diagnosis for a given scenario.
For something like melanoma which is relatively easy to diagnose with a superficial, extremely low risk skin biopsy and where early staging dramatically improves outcomes you would want to design around overcalling (high sensitivity) rather than maximize accuracy given the significant harms with false negatives and minimal harms with false positives.
An AI may be more accurate at classifying melanoma/not melanoma but if it does not meaningfully improve on the clinical threshold of biopsy/no biopsy or result in less biopsies that accuracy is wasted and may even be detrimental.
Note: I am just using this as an example to illustrate the considerations.
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.
I am a radiologist and researcher predominately focused on AI.
Throwing more intelligence at a problem doesn’t necessarily pan out financially otherwise we wouldn’t have single underemployed biology PhD.
Y2K was overblown how it was portrayed by the media but is irrelevant to the analogy of unsubstantiated extrapolation of early exponential growth.
What's your source for this? I take this flight a lot and I find it hard to believe it's more than 5.5-5.75 on average. Looking at the last few weeks for one of them[0] supports my experience.
Maybe your number has TSA/airport time included.
Health and wellness apps aren’t covered entities under HIPAA so these disclosures are not violations of it.
Time spent in a medical encounter is tied to patient satisfaction but there is rapid drop off for clinical benefit especially in the current day where investigations are more important than a physical exam in most cases and more than history in a substantial portion.
15 minutes is what we book as follow-ups or minor assessments in US+Canada, usually sufficient for most things. New consults or complex patients are 30-60 minutes.
Infodumping is not particularly helpful. Doctors are trained to use a combination of open and closed questions to guide the encounter based on their thinking and understanding of medicine. It’s relevant past medical history as not every symptom or past disease is necessarily useful in assessing what’s wrong today.
Probably incorrect in most studies, especially large population ones that influence treatment guidelines.
It’s academic and doesn’t practically matter though.
The pathogenesis of hypertension related disorders (kidney failure, heart failure, stroke etc) is well known.
It’s not in doubt that sustained hypertension is bad, that there is increased risk with higher blood pressure and that patients with high blood pressure undergoing treatment suffer less of these bad outcomes.
Acquiring the images is the hard part in obstetrical ultrasound, reporting is very mechanical for the most part and lends itself well to AI.
It’s probably very low in the context of CGM and diabetes as the potentially harmful treatments require prescriptions.
Device prescription requirements are usually due to product labelling and the manufacturers application. There are OTC fingerstick glucometers and CGMs approved.
Prenatal ultrasounds are one of the most rote and straight forward exams to get right.