61–70 of 87 posts
The point of this study is that it suggests that fully AI-automated mammography can currently deliver 70% sensitivity in detecting breast cancer using this model. It does not enable us to compare AI to unaided human performance. As this study did not include healthy controls, there is no false positive rate. The false positive rate is a crucial missing metric, since the vast majority of women do not have breast cance…
The point of this study is that it suggests that fully AI-automated mammography can currently deliver 70% sensitivity in detecting breast cancer using this model. It does not enable us to compare AI to unaided human performance. As this study did not include healthy controls, there is no false positive rate. The false positive rate is a crucial missing metric, since the vast majority of women do not have breast cance…
On the medical side, we need statistically significant tests that physicians can know and rely on - this paper was likely obsolete when it was published, depending on what "AI CAD" means in practice.
I think this impedance mismatch between disciplines is pretty interesting; any thoughts from someone who understands the med side better?
Earlier quoted context omitted.
Its interesting to see this valid argument raised against this use of AI to identify breast cancer. The lack of control groups is one of the more common concerns raised related to vaccines as well, the argument lands like a lead balloon there.
Because it's generally unethical to not give someone a treatment known already to be safe and effective. Studies of new vaccines where there is not an existing vaccine _do_ use placebo controls. Heck, my son got placebo during moderna's pediatric covid vaccine trial (to our frustration. grin.) Subsequent trials generally compare against the best known current treatment as the control instead. This study has no such c…
Earlier quoted context omitted.
They will directly write that "Radiologists miss nearly one-third of breast cancers." I trust the meaning of this article is just that it requires hospitals to rethink their decision to substitute all doctors today.
If they test someone with no background in radiology, they could even make the headline "Humans miss 50% of breast cancers"
AI doesn't have that option yet.
the setup: 1. 400s confirmed patients 2. AI reads Mammography ONLY and missed 1/3 3. on those AI missed patients, radiologists do a second read on MRI, which is the gold standard for differential. evidence: the referenced paper at the bottom
So, the whole point it (or its reference paper) is trying to make is: Mammography sucks, MRI is much better, which is a KNOWN FACT.
Now, let me give you some more background missing from the paper: 1. Why does Mammography suck? well, go google/gpt some images, its essentially X-ray for the breast, which compress 3D volumes into 2D average poole plane, which is infomation lossy. SO, AI or not, the sensitivity is limited by the modality. 2. How bad/good is Mammography AI? I would say 80~85% sensitivity agaist very thorough+experienced radiologist without making unbearable amount of FP, that probably translates to 2/3 sensitivity against real cancer cohert, so the referenced number is about right. 3. Mammography sucks, what's the point? its cheap AND fast, you can probably do walk-in and get interpretation back in hours. Whereas MRI you probably need to schedule 2 weeks ahead if not MORE. For a yearly screening, it works for the majority of polulation.
and final pro tip, 1. breast tumor is more pravelent than you think (maybe 1 in 2 by age 70+) 2. most guides recommend women with 45+ do yearly checkup 3. if you have dense breast (basically small and firm), add ultrasound screening to make sure. 4. breast feeding does good for both the mother and child, do that.
peace & love
The point of this study is that it suggests that fully AI-automated mammography can currently deliver 70% sensitivity in detecting breast cancer using this model. It does not enable us to compare AI to unaided human performance. As this study did not include healthy controls, there is no false positive rate. The false positive rate is a crucial missing metric, since the vast majority of women do not have breast cance…
Its interesting to see this valid argument raised against this use of AI to identify breast cancer. The lack of control groups is one of the more common concerns raised related to vaccines as well, the argument lands like a lead balloon there.
Not just vaccines, in each study on the effectiveness of a drug, especially when dealing with potentially life-threatening conditions, the same question is posed. From[0]:
. . . ethical guidance permit the use of placebo controls in randomized trials when scientifically indicated in four cases: (1) when there is no proven effective treatment for the condition under study; (2) when withholding treatment poses negligible risks to participants; (3) when there are compelling methodological reasons for using placebo, and withholding treatment does not pose a risk of serious harm to participants; and, more controversially, (4) when there are compelling methodological reasons for using placebo, and the research is intended to develop interventions that can be implemented in the population from which trial participants are drawn, and the trial does not require participants to forgo treatment they would otherwise receive.
This seems a bit like a needlessly publicized finding. Surely our baseline assumption is that there are lots of systems that aren't very good at finding cancer. We're interested in findings that are good. You only need 1 good system to adopt. Yes, it's good scientific hygiene to do the study and publish it going "Well, this particular thing isn't good let's move on". But my expectation is you just going until you des…
Why does this matter? Because procurement in the medical world is a pain in the ass. And no medical center wants to be dealing with 32 different startups each selling their own specific cancer detection tool.
Whats the baseline? How many did a human being get? We need to compare this to baseline to know if its good or bad