Unfortunately AI models like this one often never make it to the clinic. The model could be impressive enough to identify 100% of cases that will develop breast cancer. However if it has a false positive rate of say 5% it’s use may actually create more harm than it intends to prevent.
Another big thing to note, we recently had a different but VERY similar headline about finding typhoid early and was able to point it out more accurately than doctors could.
But when they examined the AI to see what it was doing, it turns out that it was weighing the specs of the machine being used to do the scan… An older machine means the area was likely poorer and therefore more likely to have typhoid. The AI wasn’t pointing out if someone had Typhoid it was just telling you if they were in a rich area or not.
A false positive of even 50% can mean telling the patient “they are at a higher risk of developing breast cancer and should get screened every 6 months instead of every year for the next 5 years”.
Keep in mind that women have about a 12% chance of getting breast cancer at some point in their lives. During the highest risk years its a 2 percent chamce per year, so a machine with a 50% false positive for a 5 year prediction would still only be telling like 15% of women to be screened more often.
That’s just not generally true. Mammograms are usually only recommended to women over 40. That’s because the rates of breast cancer in women under 40 are low enough that testing them would cause more harm than good thanks in part to the problem of false positives.
Nearly 4 out of 5 that progress to biopsy are benign. Nearly 4 times that are called for additional evaluation. The false positives are quite high compared to other imaging. It is designed that way, to decrease the chances of a false negative.
The false negative rate is also quite high. It will miss about 1 in 5 women with cancer. The reality is mammography is just not all that powerful as a screening tool. That’s why the criteria for who gets screened and how often has been tailored to try and ensure the benefits outweigh the risks. Although it is an ongoing debate in the medical community to determine just exactly what those criteria should be.
How would a false positive create more harm? Isn’t it better to cast a wide net and detect more possible cases? Then false negatives are the ones that worry me the most.
It’s a common problem in diagnostics and it’s why mammograms aren’t recommended to women under 40.
Let’s say you have 10,000 patients. 10 have cancer or a precancerous lesion. Your test may be able to identify all 10 of those patients. However, if it has a false positive rate of 5% that’s around 500 patients who will now get biopsies and potentially surgery that they don’t actually need. Those follow up procedures carry their own risks and harms for those 500 patients. In total, that harm may outweigh the benefit of an earlier diagnosis in those 10 patients who have cancer.
Well it’d certainly benefit the medical industry. They’d be saddling tons of patients with surgeries, chemotherapy, mastectomy, and other treatments, “because doctor-GPT said so.”
But imagine being a patient getting physically and emotionally altered, plunged into irrecoverable debt, distressing your family, and it all being a whoopsy by some black-box software.
That’s a good point, that it could burden the system, but why would you ever put someone on chemotherapy for the model described in the paper? It seems more like it could burden the system by increasing the number of patients doing more frequent screening. Someone has to pay for all those docter-patient and meeting hours for sure. But the benefit outweighs this cost (which in my opinion is good and cheap since it prevents future treatment at later stages that are expensive).
You have really good point too! Maybe just an indication of higher risk, and just saying “Hey, screening more often couldn’t hurt.” Might actually be a net positive, and wouldn’t warrant such extreme measures unless it was positively identified by, hopefully, human professionals.
You’re right though, there always seems to be more demand than supply for anything medicine related. Not to mention, here in the U.S for example, needless extra screenings could also heavily impact a lot of people.
Unfortunately AI models like this one often never make it to the clinic. The model could be impressive enough to identify 100% of cases that will develop breast cancer. However if it has a false positive rate of say 5% it’s use may actually create more harm than it intends to prevent.
Another big thing to note, we recently had a different but VERY similar headline about finding typhoid early and was able to point it out more accurately than doctors could.
But when they examined the AI to see what it was doing, it turns out that it was weighing the specs of the machine being used to do the scan… An older machine means the area was likely poorer and therefore more likely to have typhoid. The AI wasn’t pointing out if someone had Typhoid it was just telling you if they were in a rich area or not.
That’s why these systems should never be used as the sole decision makers, but instead work as a tool to help the professionals make better decisions.
Keep the human in the loop!
Not at all, in this case.
A false positive of even 50% can mean telling the patient “they are at a higher risk of developing breast cancer and should get screened every 6 months instead of every year for the next 5 years”.
Keep in mind that women have about a 12% chance of getting breast cancer at some point in their lives. During the highest risk years its a 2 percent chamce per year, so a machine with a 50% false positive for a 5 year prediction would still only be telling like 15% of women to be screened more often.
Breast imaging already relys on a high false positive rate. False positives are way better than false negatives in this case.
That’s just not generally true. Mammograms are usually only recommended to women over 40. That’s because the rates of breast cancer in women under 40 are low enough that testing them would cause more harm than good thanks in part to the problem of false positives.
Nearly 4 out of 5 that progress to biopsy are benign. Nearly 4 times that are called for additional evaluation. The false positives are quite high compared to other imaging. It is designed that way, to decrease the chances of a false negative.
The false negative rate is also quite high. It will miss about 1 in 5 women with cancer. The reality is mammography is just not all that powerful as a screening tool. That’s why the criteria for who gets screened and how often has been tailored to try and ensure the benefits outweigh the risks. Although it is an ongoing debate in the medical community to determine just exactly what those criteria should be.
How would a false positive create more harm? Isn’t it better to cast a wide net and detect more possible cases? Then false negatives are the ones that worry me the most.
It’s a common problem in diagnostics and it’s why mammograms aren’t recommended to women under 40.
Let’s say you have 10,000 patients. 10 have cancer or a precancerous lesion. Your test may be able to identify all 10 of those patients. However, if it has a false positive rate of 5% that’s around 500 patients who will now get biopsies and potentially surgery that they don’t actually need. Those follow up procedures carry their own risks and harms for those 500 patients. In total, that harm may outweigh the benefit of an earlier diagnosis in those 10 patients who have cancer.
Well it’d certainly benefit the medical industry. They’d be saddling tons of patients with surgeries, chemotherapy, mastectomy, and other treatments, “because doctor-GPT said so.”
But imagine being a patient getting physically and emotionally altered, plunged into irrecoverable debt, distressing your family, and it all being a whoopsy by some black-box software.
That’s a good point, that it could burden the system, but why would you ever put someone on chemotherapy for the model described in the paper? It seems more like it could burden the system by increasing the number of patients doing more frequent screening. Someone has to pay for all those docter-patient and meeting hours for sure. But the benefit outweighs this cost (which in my opinion is good and cheap since it prevents future treatment at later stages that are expensive).
You have really good point too! Maybe just an indication of higher risk, and just saying “Hey, screening more often couldn’t hurt.” Might actually be a net positive, and wouldn’t warrant such extreme measures unless it was positively identified by, hopefully, human professionals.
You’re right though, there always seems to be more demand than supply for anything medicine related. Not to mention, here in the U.S for example, needless extra screenings could also heavily impact a lot of people.
There’s a lot to be considered here.
deleted by creator