AI Fares Better Than Doctors at Predicting Deadly Complications After Surgery
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AI in HealthcareMedical PredictionSurgical Complications
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AI in Healthcare
Medical Prediction
Surgical Complications
A study found that an AI model outperformed traditional risk scores used by doctors in predicting post-surgery complications, sparking discussion on the role of AI in healthcare and the limitations of human judgment.
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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.
>...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.
You need to ask do you prefer better black box or weaker white box which you can understand and reason about. For many tasks black box is fine. For this I wonder which one I would prefer...
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
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
If you actually need a really high risk surgery, you probably have a terrible prognosis without it
For instance, in the pivotal trial of transcatheter aortic valve replacement for aortic stenosis (TAVR) the people were deemed too high risk for surgery, so got nothing (well, medicine only which doesn't really change anything for this condition) or TAVR. The medicine arm had 50% mortality (1 year I think?) whereas the TAVR arm was "only" 30%!
Now that didn't mean all those 30% of deaths were due to the procedure or even the aortic stenosis. I think that ran 10% or so (going off memory here). They just had so many other problems. For comparison, TAVR is now done in low-risk people, and I think the 1 year mortality is <3%
The things that go into making someone "high risk" in the STS (cardiac surgery) risk score are for the most part pretty obvious. If your heart muscle is super weak (or you need a machine to keep going before surgery), you have kidney failure, prior strokes, combined heart problems, bad liver or lung disease, etc etc. You can calculate a score, but you probably can guess it from the door of the room
Most people think ChatGPT == AI Whereas this is a specially trained model tuned to this exact use case.