AI in medicine will help doctors, not replace them
blogs.scientificamerican.com
blogs.scientificamerican.com
If your job is automated away, what are you going to do? Hypothetically let's say that AI can accomplish everything that an X-ray tech can (or that with the help of automation a single tech can do what 10 techs do today). What happens to those people? They spent a lot of time and money getting that training. Retraining programs suck and are shown to be really ineffective. So do these people just go underemployed the rest of their lives? What about people who are 10-15 years from retirement? Even if retrained you go from peak earnings to starting wages. That's a huge disrupt in life.
Job loss is extremely important to consider (assuming you care about people), even if the total number of jobs are increased. We've seen this in the past and we're seeing it today. Lots of automation came into farming and many of those jobs disappeared. Family legacies were lost. But at the same time it is inhumane to not allow the progression of technology (like we could even stop it...).
My concern is that people aren't discussing the transitions of economies. We all want to live in a post scarcity world like Star Trek. Where food, housing, and basic essentials are effectively trivially obtained (for the most part in the show). But transitioning to that kind of society is extremely disruptive and has a lot of pitfalls on the way. It isn't unimaginable to ask "What if 10% of your population is unemployable?" (several scenarios: jobs just automated away; only existing jobs require high skill and training; transition period where those people are trained for jobs that recently got automated away; etc) What do we do? How do we handle that? We're a society where people still believe your worth correlates with your wealth (diction intended). I see very few people talking about this, and these kinds of scenarios are plausible within the next 50-100 years.
Net gains or losses of jobs is a distracting question and the wrong one to ask.
Some things that would help a whole lot:
1. We need to address our housing issues.
This isn't necessarily disastrous if you can move to some little hole in the wall in a walkable area with good transit so you can still have a life while living on meager earnings and retraining. The problem is that we've torn down about a million SROs in the US and the average size of new housing has more than doubled since the 1950s, so there just aren't enough places like that.
2. We need to resolve our healthcare issues in the US.
Healthcare costs something like 20% of GDP and disproportionately negatively impacts poor people, unemployed people and people with chronic health issues (who are often pushed into poverty by that fact). If you can go to a doctor no matter how poor you are, these problems are vastly less likely to snowball out of control.
3. We need to embrace gig work and figure out how to make it a positive instead of decrying it and vilifying it.
When I was deathly ill and homeless, gig work was a godsend. My earning capacity gradually went up and I eventually got back into housing. I still struggle, but it's better than it used to be.
Gig work allowed me to develop an earned income and marketable skills at a time and under circumstances where no regular job would have worked, yet all I seem to ever hear is how evil it is. It's not inherently evil, though certainly some gig work is handled in a problematic fashion that keeps workers trapped in a dead end.
I blame the banks to a large degree. 30% of income for 30 years is obscene cost for a house. I am committed to mortgage free living.
I'm sorry to hear about your struggles and very glad you are doing better.
In every other article, there's a dichotomy between "everyone will keep their jobs and be more efficient and have more time to focus on what matters" and "everyone will lose their jobs and be replace by machines". This then gets resolved to "we will never be replaced because 'human factors'", therefore option a).
I am sure the author knows their field well, but this just doesn't seem to provide any interesting / new viewpoint on the issue beyond the arguments that usually come up in superficial discussion of the topic.
Joking aside, I think for a lot of medical stuff the shift is already happening- there are less people going into certain fields (such as radiology) due in part to fear that it isn't a long term career. This is driving up radiology pay as the demand is outpacing the incoming supply.
While I ultimately believe that most things are going to get automated, I do agree that in the short to medium term were going to see AI augmenting medical professionals rather than replacing them. This is just the natural progression of things- the technology can start off with the low hanging fruit and gradually take on more and more of the work. This provides immediate benefit while building funding and knowledge that can be used to take the next big step.
If AI can lower the bar to the point where Ned the Nitwit can type in the symptoms, read the screen and get a reasonable diagnosis then 'doctors' are going to be a completely different class of people even if they share the same title.
Most people probably only need a yearly-visit to the physician's assistant or a nurse for bloodworks. There shouldn't be a need to see an actual doctor on a regular basis (unless you have some chronic ailment that nurses / physician assistants can't handle).
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The prescription is probably the only part of that routine that requires a doctor.
50% of doctors graduate in the lower half of their class each year. Lots of low hanging fruit with ML if implemented properly (big if, I know).
Unfortunately, that is of very little use to the clinician. The big hype is a consequence of a big misunderstanding of the practical constraints of healthcare.
Time and effort don't fix bad data and bad statisticians.
Like, if there is a problem with their performance, it is a problem that they are graduating. And then look at the group that is admitted. It isn't a low bar.
How much of an average doctor's day is really appointments like this where they just check you lab values and ask you if you're doing okay on your medication and send you on your way? A large number of these cases are already handled by nurse practitioners and PAs. Doctors in my experience spend more time seeing the sicker patients with many chronic medical conditions where the more in depth knowledge of pathology and pharmacology that they spend those years of training acquiring inform the questions they ask, the tests the order, and the physical exam they preform.
I don't think the large majority of PAs and NPs really want to step into the same role as physicians. The number of PAs and NPs who were rejected from medical school and then chose to take either of those routes is probably very small compared to the number who chose to become a PA or NP because they wanted that role and not a doctor's.
This is not about replacing people wholesale, but giving them better tools.
Nobody here is against that.
It's really a political problem.
People don't seem to have enough motivation to politically fight to replace doctors with nurses, and doctors have a lot of power and motivation to keep their jobs.
And what about powerful actors from inside the system ? Hospitals, Insurers, etc ? In general, everyone gets a share of revenue. So why bother reducing that ?
physicians usually hit their stride 5-10 years before they kick the bucket.
Along with that Ned is probably fooling himself already. It takes a skilled human to ask the kind of non leading questions that might even lead to the truth of what the problem is.
So far Picasso's statement still holds true "Computers are useless, they can only give you answers" (ok "useless" is an unfair accusation. Answers are useful but you need a human to ask the question and judge the answer. Picasso's statement holds true for art though.)
And normal doctors do not? Are they infallible? Does papal infallibility apply to doctors to? I know a bunch of doctors, they aren't all that different from normal humans except in Australia we work them until they are sleep deprived. They will be making potentially lethal mistakes continuously and it isn't being detected. Every so often there is a scandal when an egregiously bad doctor is found.
Consistent bias and repeatable mistakes are a lot safer to work with than random bias and random mistakes.
We have a lot of examples where humans could initially compete with machines until one day they no longer could. Patient analysis has hallmarks of being a solvable problem (pattern recognition, image interpretation, consuming huge amounts of data, etc). If someone is allowed to compete it will end well for everyone. Once that goes 'doctor' is a title that means something different to what it does now.
Also Australian, not a clinician but I work in health.
Where I have seen the best use of computers working hand-in-hand with doctors is around the areas of clinical risk and funding.
Computer systems that prevents a clinician from prescribing a lethal dose, or prevents a known adverse interaction between drugs have already saved lives in Australia. In hospitals which have properly implemented these systems, drug mistakes have dropped dramatically.
On the funding side, I have found that clinicians are very interested in ensuring that the maximum public funds are available for their patients as soon as possible. If a patient fulfills the requirements for a funding stream, the clinician wants to know about that immediately so that they can start providing their patients with those services. This is especially important in community care situations where patients can receive at-home support to maintain adequate cleanliness and hygiene in order to keep them out of hospital (hospital being bad for the patient and bad for the public purse).
That AI could diagnose many diseases before there are any visible symptoms.
How is m*modal, have you used it?
At Kaiser Permanente, I regularly train models on 10s/100s M patient/dr encounters. Our transformer language models are fine tuned on multi-billion word corpuses. Some of our models do real time inference on millions of patient notes per month.
The thing is, from data governance standpoint our org, and most other health orgs, just aren't comfortable sharing this data with outside businesses or even each other. And of course orgs like KP strongly don't believe in licensing internally developed products to other health orgs.
I know we generate a lot of data. I also know it's data that's so unreliable that its business value does not lie in its real-world use for improving healthcare pathways. It's very valuable politically and from a managerial standpoint, though. Unfortunately.
It's necessary but not sufficient. The biggest problem in most applications is labeling, which either doesn't exist at all or is insufficient in most clinical workflows.
Then consider the amount of not-doctor roles in medicine which can be automated/assisted
A doctor's appointment schedule is crazy busy, with "LONG" appointments being under 20min per patient. Imagine a scenario where a doctor is able to spend time with the patient/their medical history etc.
I spend a lot of time going to doctors appointments these days, and a large number of the doctors that I see, now have an assistant/transcriptionist in the room to manage the EMR/charting app, because that enables them to focus on the patient.
So they want to minimize the time they spend on each and prefer to give a second appointment months later
The concept of sensing and learning from surroundings should survive.
Human's feed AI, AI should go interstellar.
But what we will have to make sure is are we feeding the AI the right things?
Nope, Tech giants, once built by nerds are now ruled by political criminal minds.
Without living in harmony there is no way AI will serve only good purpose.
If you are a developer, think thorough whom are you helping, why they need you. The code you write is a part of your brain. Have self righteous that code should be used for good and good only.
How will you guarantee that? you cant. Then don't innovate!.
Stop being the old school nerd boy and level up. Do not show off and gain attention unless you fully solve the problem.
Circle back to start. Question again and again how this will be used flawlessly. If it doesn't its not worth sharing knowledge. Share morals.