AI models miss disease in Black and female patients

A few questions for you to think of then -- or rather a few things I think you should consider with your statements:

1. How does ACA affect the corpus of knowledge and medical practice gathered prior to the ACA being in effect? How does it affect late 19th, and early and mid 20th century medical knowledge and practice, which occurred prior to health insurance of any kind, nevermind ACA-compliant, being widespread? This corpus of knowledge and practice continues to propagate even now. I've read a handful of recently published medical textbooks and there are definitely parts that are pretty much the same as the textbooks of the early 20th century, just with slightly updated language.

2. What are the possible confounding factors in the use of health insurance by men vs women? For example, could men just be more hesitant to see a doctor, and thus less likely to make use of health insurance? Does the average life expectancy of women result in more use of health insurance later in life than for men? Are medical procedures that are specific to women that add to the cost of their care, such as mammograms, pap smears, etc? Seeings as how in the US health insurance is a practical requirement to getting medical care, and lack of it is punished financially in various ways from taxes to just having medical care be more expensive when you truly need it, means most people will try to have _some_ kind of health insurance, even if they don't think they need it for actual health reasons. So despite a perception of not needing health insurance, men are incentivized to have health insurance they don't use?

3. Does the ACA guarantee in any way that medical professionals no longer hold any bias due their previous training, especially if such training occurred prior to the introduction of the ACA? Does the ACA similarly guarantee that women and men are not only able, but choose to pursue medical care and participate in medical studies at percentages matching the general population?

Your point about men subsidizing women with regard to health insurance premiums may be perfectly valid, I am not disputing you on that point. I am disputing that it is salient to the tradition and practice of medicine in the western world in the modern era, until very recently historically, and that these traditions and biases will affect data sets gathered from people who are directly affected by these biases and traditions to this day. We haven't eliminated them, because as I said in another comment, every generation just dilutes the old issues, it doesn't solve them. And while I could spend my evening finding studies from various countries that attest to my view on this, I have spent about as much time as I desire to on this, so I will grant you that my evidence is on the level of 'trust me bro' -- with the slight caveat that many people within just my family and close circle of friends are involved in the medical field and all largely agree to this, and they are not all based in the US (which by the way, your point is very specific to. ACA is a US thing, western medicine spans a bit more than that.) It is entirely fair for you to call out that I have offered no real peer-reviewed evidence for my statements. I intend to offer a viewpoint of someone who has had extensive peripheral experience with medical professionals and has discussed this topic with them, and to offer some avenues of thought on how and why the data sets might be biased.

Here is an academic medicine perspective: https://www.aamc.org/news/why-we-know-so-little-about-women-...

To give you some TL;DR from personal-ish experience, women have historically been excluded from medical trials because:

* why include them? people are people, right? * except when they're pregnant or could be pregnant -- a trial by definition has risks, and so "of course" one would want to exclude anyone who is or could get pregnant (it's the clinical trial version of "she's just going to get married and leave the job anyway") * and cyclical fluctuations in hormones are annoying.

The first one is wrong (tho is an oversight that many had for years, assuming for instance that heart attacks and autism would present with the same symptoms in all adult humans).

The second is an un-nuanced approach to risk. Pregnant ladies also need medical treatment for things, and it's pretty annoying to be pregnant and be told that you need to decide among unstudied treatments for some non-pregnancy-related problem.

The third is just a difficult fact of life. I know researchers studying elite performance in women athletes, for instance. At an elite level, it would be useful to understand if there are different effects of training (strength, speed, endurance) at different times in the menstrual cycle. To do this, you need to measure hormone levels in the blood to establish on a scientific basis where in the cycle a study participant is. Turns out there is significant heterogeneity in how this process works. So some scientists in the field are arguing that studies should only be conducted on women who are experiencing "normal menstrual cycles" which is defined by them as three continuous months of a cycle between 28-35 days. So to establish that then you've got to get these ladies in for three months before the study can even start, getting these hormone levels measured to establish that the cycle is "normal", before you can even start your intervention. (Ain't no one got $$ for that...) And that's before we bring in the fact that many women performing on an elite level in sport don't have a normal menstrual cycle. But from the sports side, they'd still like to know what training is most effective.... so that's a very current debate in the field. And I haven't even started on hormonal birth control! Birth control provides a base level of hormone circulating in the blood, but if it's from a pill it's varying on a daily basis, while if it's a patch or ring it's on a monthly basis (or longer). There's some question of whether that hormonal load from the birth control is then suppressing natural production of some hormones. And why does this matter? Because estrogen for instance has significant effects on cardiovascular health, being cardioprotective from puberty up to menopause. (Yeah, I didn't even get started on perimenopause or menopause.)

Fine, fine, it's just data analysis & logistics. If you get the ladies (only between 21-35) into the lab for blood samples frequently enough and measure at the same time of day every time to avoid daily effects and find a large enough group that you can dump all the ladies who don't fit some definition of normal & anyone who gets pregnant but still get the power for your study, it's all fine, right? You've just expanded medical research to incorporate, like, 10% more of the population....!

Breast density affects the imaging you get from x-rays. It is well-known that denser breast tissue results in x-rays that are "whiter" (I'm talking about the image of the tissue, in white, on a black background, as x-rays are commonly read by radiologists). Denser breasts are associated with less effective screening for breast cancer via mammogram. A mammogram is a low-dose x-ray.

When using a chest x-ray to look for pulmonary edema, for instance, I would be unsurprised if breast tissue (of any quantity) and in particular denser breast tissue would make the diagnosis of pulmonary edema more difficult from the image alone.

Also, you seem to have conflated a few things in your second sentence. Deep in the article, they did have radiologists try to guess demographic attributes by looking at the x-ray images. They were pretty good at guessing female/male (unsurprising) and were not really able to guess age or race. So I'm super interested in how the AI model was able to be better at that than the human radiologists.

Then why is the headline not "AI models miss disease in Asian patients" or even "AI models miss disease in Latino patients"?

It just so happens to align with what maximizes political capital in today's world.