Dr Khullar suggests that AI will exacerbate biases in medical practice. His fundamental concern is that machine learning will codify biases and become self-fulfilling prophesies. But there is scant evidence that AI will worsen these disparities.
If anything, a machine-learning point of view better addresses his concerns than a traditional one, because they can be much more quickly updated to correct for identified biases. Doctors spend years and years of hard work becoming efficient and effective human algorithms themselves, and updating those human algorithms in the face of newer evidence is difficult. In standard practice, biases are often invisible and uncodified to begin with. "Moral intuition" is something all doctors use, but it's also something of a black box in nearly every real-world use case.
I'm speaking as former CTO/Co-Founder of medical image ML firm (for 3yrs):
1. there is already a major bias in medical diagnosis - a bias favoring those who can actually pay
2. automating even parts of the diagnostic process saves money and reduces cost, that is a huge benefit to everyone
3. not everything gets done immediately. lets figure out the basics first (getting classifiers working on whatever dataset we have) and then focus on getting it to work on everything. It isnt like medicine was right from day one...heck, I seem to recall leeches and bloodletting being the norm for a long time.
4. Almost every doctor i spoke to was afraid of ML/AI because it pierced their forced scarcity and threatened their wages. I might argue that Health Disparities are worsened currently because medical boards throttle residency programs and fellowships to create an artificially constrained supply and hence high prices. (before I get the rot response of...of course doctors will never go away...:yes, they wont go away, but they will focus less on rote things and increase throughput thus increase supply thus decrease wages.
5. We got all our training data from minorities. Incidentally, foreign countries are a lot more generous with training data. For our ML diagnostic firm, we had envisioned giving the product away for free in poorer countries where we could just get training data.
>4. Almost every doctor i spoke to was afraid of ML/AI because it pierced their forced scarcity and threatened their wages. I might argue that Health Disparities are worsened currently because medical boards throttle residency programs and fellowships to create an artificially constrained supply and hence high prices.'
>> Medical boards don't control residency programs, hospitals and the federal government do.
Medical boards are the only ones who can train and grant residencies. The US Government cannot train nor can they grant someone a residency. Nor can the US Government grant someone a fellowship. As an example, to become a "Fellow of American College of Surgeons" you would apply here: https://www.facs.org/member-services/join/international
The cartel then decides who gets to join the select group. There are three dozen such cartels, one for dermatology, one for radiology, etc etc. Nowhere in the process noted on the link does it specify the US government gets to decide membership.
The government can fund it, but anyone can fund it. The funding is minuscule -- the 2017 resident salary is $57k a single digit percentage of billings by residents.
The medical board can train and grant residencies, but they don't control how many actual-working-in-a-hospital residency slots are available, downstream of them.
(There are never enough.)
Anyone can fund it, just like anyone can fund filling in the potholes in front of my apartment. For some reason, though, until the government gets around to it, they aren't going to get filled.
No one fixes random potholes because they dont get paid to do it. If fixing potholes had a 500% profit margin as medicine does, people would start fixing them immediately.
Now, if "The American Fellowship of Pothole Fixers" claimed there was no money to fix potholes and only their coveted group of 300 pothole fixers are allowed to fix potholes, i'd call BS.
In the US, the average resident makes ~57k USD these days. If you're familiar with medical bill rates in the US, a week of billings covers the entire annual salary. For specialists (e.g., derm, radiology, etc) a day of billing can cover the entire annual salary for the resident. Even if you assume not all bills are collected, or that many are negotiated down by insurers, the profit margin on residents is off the charts.
Given billing rates, "we dont have money" is a very convenient answer for why there arent more residents (and hence more future supply of doctors.) Heck, given the wild profit of a resident, I'd personally fund their annual salary for a share of the annual billings.
The real answer is...current doctors, specifically specialty boards must actually be willing to train a resident, however they are funded (medicare, by hospitals, by me, etc.) -- and specialty boards do not. It would increase supply and decrease their future wages. Openings are very carefully throttled to create artificial scarcity.
Medical specialty boards are essentially cartels.
This is hard to imagine as a technologist because we largely operate in a free market. Anyone can enter the market and opt to work for less money than you. A foreign worker can try to do your job for less. The job can be off-shored.
@nradov - i'd love to understand your point more, but all you've shown is that there is some funding gap for residency programs. Funding gaps exist for unprofitable things where you dont get back immediate money for each dollar you put in. There is a funding gap for the arts, for urban preservation, etc.
Medical resident positions are wildly profitable entities, so "funding gap" sounds like a boogieman excuse.
Medical resident positions are so wildly profitable, that if any medical board was willing to train residents/fellows, i'm certain I can get VC/PE/HF funding to fund those spots and no one would have to worry about funding gaps. Who wouldn't want to fund a position that produces 10x revenues?!
I'd be willing to bet that for many residencies (cosmetic derm, spinal surgery, ortho, radioiology) residents would be willing to work for absolutely free given the massive windfall they expect in 5yrs time.
"Medicare funding gaps" are boogieman excuses provided by the AMA and medical specialty boards to not train doctors, especially specialists and sub-specialists and create artificial scarcity and increase their own wages.
Medical boards don't train residents at all. And board certification isn't even required to practice medicine; it's entirely optional. You're complaining about the wrong problem.
Residents are trained in teaching hospitals, most of which are non-profit. So VC/PE/HF funding isn't applicable. The federal government provides the majority of funding for residency slots and there is a hard cap.
Teaching hospitals certainly could fund more resident slots themselves but they generally choose to spend their money on other priorities like new MRI machines or free care for indigent patients or shiny new buildings named for major donors. Hospital budgeting decisions are made by business executives and BoD members just like any enterprise; they aren't controlled by the AMA or medical boards.
Training residents isn't as profitable as you think; there are huge overhead expenses for supervision, insurance, equipment, and support staff. But if you don't believe me then feel free to get VC funding, found a new for-profit teaching hospital, obtain AGCME accreditation, and hire a thousand residents. I expect you'll find the economics don't work but maybe you'll disrupt the industry and make a fortune?
Do technologists truly operate in a free market? There are rampant anti-competitive practices across tech, I think it's a SV libertarian fantasy that they are in a free market, a fantasy they tell themselves to paper over their squashing of rivals.
The job market is very competitive. You don't need anyone's permission to enter it, all you have to do is do good work. Salaries are high due to a combination of massive demand and the fact that it takes a long time to get good at it. Even their stupid collusion attempts are basically fruitless, because the tech market isn't just four colluding companies, there are thousands. You don't have to go from Google to Apple, you can go to Amazon or Red Hat or numerous others, or create your own startup. That number of companies could never secretly collude -- they couldn't even get away with four. Which is why salaries are still high.
The true threat is companies crushing smaller rivals, because that's how in the long term you end up in a situation where there aren't thousands of tech companies because no one can compete without the assent of one of the major ones, and they prefer to destroy you or compete with you or buy you out than let you grow independently. And that's how salaries could fall in the long-term. But you tell people that supporting walled gardens and closed proprietary services could lower their long-term salary and they don't hear you, because they're after the quick buck today.
We’re on this forum because we, as little guys, can often beat the big guys at things. It’s more of a free market than most things. Though nothing is a completely unregulated market.
Damn, funny to see how these things are perceived years after the event. The residency cap didn’t coincidentally exist. The AMA pushed for it and they pushed for similar things in universities https://usatoday30.usatoday.com/news/health/2005-03-02-docto...
The AMA is actually evil. They’ve probably killed people in America though their protectionism. A truly banal evil : the pursuit of increased physician salaries.
Fairness in AI/ML has been a huge talking point over the last 2 years in the community. I know of around 2 panels/conferences with major industry/academic participation in the US that are scheduled in the next few months.
Contrary to the image of mathematicians being rather consequence averse metric driven people, I have found University labs place a large emphasis on trying make sure their models do not have such biases.
It is a serious issue worth attention, but the response from the community has been prompt.
If anything, a machine-learning point of view better addresses his concerns than a traditional one, because they can be much more quickly updated to correct for identified biases. Doctors spend years and years of hard work becoming efficient and effective human algorithms themselves, and updating those human algorithms in the face of newer evidence is difficult. In standard practice, biases are often invisible and uncodified to begin with. "Moral intuition" is something all doctors use, but it's also something of a black box in nearly every real-world use case.