I actually think ML models would excel here. Humans are famously bad at estimating and weighing risks and there's really only so much data a single human brain can store and draw conclusions from. Not to mention bias like female patients being chronically under-diagnosed by male doctors.
If you fed a mountain of surgery outcome data into an ML model, I imagine it'd be shockingly effective and (hopefully) less biased on sex and race.
It'd probably be helpful for initial diagnosis, but I'm less confident in that. Postop risk assessment is mostly straight statistics, and statistical inference is what ML models do. Diagnosis is a bit more subjective and complex, though it is in the same general domain.
The real trick is going to be conditioning doctors to not blindly trust the risk assessment model. Though I would hope that it'd be accurate enough for that anyway
The article mentions a previously existing risk scoring system, which was presumably already trying to deal with the problem of humans not being great at evaluating the risk.
>...the body of the article doesn’t describe a panel of physicians making predictions at all. The headline says “AI fares better than doctors,” but the text says the model outperformed “risk scores currently relied upon by doctors,” i.e., standard scoring tools clinicians use—not the judgments of the surgeons on the case or an outside panel.
I get the feeling that this is one of those things where you s/AI/statistics/g. Doctors using a predictive statistical model trained on thousands of patients' worth of data faring better than doctors using the seat of their pants makes total sense.
1) No, machine learning perfoms better than typical "risk scores" such as the RCRI (it was not tested against doctors clinical judgement)
2) Even so...so what? What we don't have is any reliable way to reduce surgical complications when the benefit outweighs the risk when the risk is elevated
Human doctors have a tendency to underestimate their own complication rate, often because they are too delusional about their own capabilities. I've heard the same doctor say "this has never happened to me in my 20 years of doing surgery" twice, when a complication occurs during a surgical procedure.
The strange thing is that this potentially life-saving tech will only collect dust because AI in medicine is only good for papers but not for real world usage. See all other AI medicine advancements. Same pattern. Medicine has a problem of not being willed to use modern tech to save lives.
The headline definitely evokes such an idea, but the detail in the article simply shows the machine learning system better augmenting the doctors' work
"Fares Better" sounds unscientific and very much like click bait
In cases where the numbers suggest that the average treated person "Fares better" than barely over 50% of the control group, or when effects are inconsistent, readers may not interpret the effects as profound.
Providing real numbers that are easily understandable, rather than evocative descriptions, allows readers to form their own conclusions about the results.
I actually think ML models would excel here. Humans are famously bad at estimating and weighing risks and there's really only so much data a single human brain can store and draw conclusions from. Not to mention bias like female patients being chronically under-diagnosed by male doctors.
If you fed a mountain of surgery outcome data into an ML model, I imagine it'd be shockingly effective and (hopefully) less biased on sex and race.
It'd probably be helpful for initial diagnosis, but I'm less confident in that. Postop risk assessment is mostly straight statistics, and statistical inference is what ML models do. Diagnosis is a bit more subjective and complex, though it is in the same general domain.
The real trick is going to be conditioning doctors to not blindly trust the risk assessment model. Though I would hope that it'd be accurate enough for that anyway
The article mentions a previously existing risk scoring system, which was presumably already trying to deal with the problem of humans not being great at evaluating the risk.
AI seems to explain this better than as framed:
>...the body of the article doesn’t describe a panel of physicians making predictions at all. The headline says “AI fares better than doctors,” but the text says the model outperformed “risk scores currently relied upon by doctors,” i.e., standard scoring tools clinicians use—not the judgments of the surgeons on the case or an outside panel.
I get the feeling that this is one of those things where you s/AI/statistics/g. Doctors using a predictive statistical model trained on thousands of patients' worth of data faring better than doctors using the seat of their pants makes total sense.
1) No, machine learning perfoms better than typical "risk scores" such as the RCRI (it was not tested against doctors clinical judgement)
2) Even so...so what? What we don't have is any reliable way to reduce surgical complications when the benefit outweighs the risk when the risk is elevated
Human doctors have a tendency to underestimate their own complication rate, often because they are too delusional about their own capabilities. I've heard the same doctor say "this has never happened to me in my 20 years of doing surgery" twice, when a complication occurs during a surgical procedure.
The strange thing is that such articles always evoke the idea that AI is replacing humans even in serious work, which is frightening.
The strange thing is that this potentially life-saving tech will only collect dust because AI in medicine is only good for papers but not for real world usage. See all other AI medicine advancements. Same pattern. Medicine has a problem of not being willed to use modern tech to save lives.
I can understand reticence to basing conclusions about people's health on minimal evidence.
The headline definitely evokes such an idea, but the detail in the article simply shows the machine learning system better augmenting the doctors' work
Eh, this is about Luddite statistical models and not real AI (chatbots).
Let's see how well it draws an SVG of a pelican on a bicycle
We need better words. This isn’t a chatbot.
Most people think ChatGPT == AI Whereas this is a specially trained model tuned to this exact use case.
Until we build in the same financial bias...
"Fares Better" sounds unscientific and very much like click bait
In cases where the numbers suggest that the average treated person "Fares better" than barely over 50% of the control group, or when effects are inconsistent, readers may not interpret the effects as profound.
Providing real numbers that are easily understandable, rather than evocative descriptions, allows readers to form their own conclusions about the results.
It says that doctors could predict accurately if a patient would die after surgery 60% of the time, and AI 85% of the time.
If a surgery is extremely risky, the doctors probably won't do it... so there's a systemic bias here in the data.