A few further thoughts about NVIDIA CEO Jensen Huang’s farcical description of the impact AI has “already” had in completely revamping the field of radiology.
Huang presents radiology as the “evidence” of what the near-future impact of AI on the workforce will be. I’ll include the quote again in this post for completeness:
One thing that I will say, give you some evidence, is that, and I was just telling Elon about this earlier, radiology, for example, has largely been converted to AI-driven radiology. And there’s some really great companies doing that. And the surprising thing is the prediction that all radiologists would be the first jobs to go was exactly the opposite. The trend shows that there are more radiologists being hired now as a result of AI.
And the reason for that, if you take a step back, it’s because the goal of a radiologist is not to study the images. The goal of a radiologist is to diagnose a disease. Now the studying of the images became so productive they could study more images, study more modalities, spend more time with the patients, and as a result, they were actually accepting more patients. We’re doing more radiology all around the world, we’re doing a better job with diagnosing disease.
Again, none of that is even merely an exaggeration. It’s just wholly untrue as to the current use of AI in radiology, let alone the impact far enough in the past to have percolated through and already changed how the whole field practices.
This is a man personally worth almost $200 billion in charge of a company with a market cap over $4 trillion. One imagines he has access to reality if desired.
I’ve seen the clip shared countless places by credulous people who don’t know any better. This is not to say these things won’t happen, but the use of the past tense is a real problem. I think it’s worth putting the fantasy in context.
Radiology Isn’t Illustrating Jevons’ Paradox
Jevons Paradox is the observation that technological advancements that increase resource efficiency can counterintuitively lead to an overall increase in resource consumption. Jevons’ original formulation concerned improving coal technology leading to paradoxically increased demand/consumption of coal. As in, when it’s cheaper, you can buy more and do more.
Huang is parroting a recently commonly invoked human radiology analogy: AI makes radiologists more efficient in interpreting scans, more scans somehow get done, and voila demand magically increases.
The problem is this doesn’t reflect reality, at least not currently. Imaging volumes have been increasing steadily for decades. Scan acquisition time has nothing to do with scan interpretation time (the former actually ironically is benefiting from machine acceleration in some situations). Interpreting efficiency has barely moved, if at all, and turnaround times are actually lengthening amidst unmanageable volumes. Increased demand for radiologists is just the secular shortage of qualified radiologists struggling to keep up with organically increasing volumes. AI has nothing to do with it.
To further compound how much we are not in Jevons’ land yet: reimbursement for radiologist professional services is stable to falling from payors (again, not related to efficiency), but radiologists have actually cost the system more recently as they demand stipends from hospital systems to provide continued access to services. More simple supply/demand at work. Those scans, even the “more efficient” ones, do not cost less for patients or payors yet. More efficient MRIs and radiologists are both paid the same on a per-study basis.
There are some AI tools for radiologists with reasonable market penetration: variations on list triage (enabling potentially positive scans to jump the queue for interpretation) and generative draft impressions derived from the findings section of the report. Meaningful computer vision (beyond narrow pathologies like brain bleeds, blood clots, fracture detection) is frankly not in particularly widespread use despite what is parroted by tech companies using the news as free marketing, and so far any purported efficiency benefits have been unconvincing. A mediocre tool allows careless people to move faster but gives careful radiologists just another thing to review. So far, the state of the art has been mostly a wash.
Again, this isn’t one of those “AI could never do what my amazing organic brain can do!”; it’s an “AI really certainly hasn’t yet in real practice, and nothing on the market so far has really moved the needle.”
Greater efficiency could trickle down to depress prices in the future and/or eventually lead to increased demand for imaging, but it hasn’t happened yet, and it’s unclear if it did that it would lead to increased demand for the human component. Healthcare is not really a free market. No one in healthcare is the equivalent of coal.
Is it possible AI will lead to automated scan acquisition and instantaneous scan interpretation a la Star Trek, and we will suddenly not just want but need all scans all the time, and everyone will be building more and more machines to scan more and more people because interpretive costs are down, and physical throughput is the real bottleneck? Sure. We can just put a conveyor belt at the entrance to the ER!
Is it also possible that—instead of a Jevons process—professional fees could crash as radiologists become box-checking liability-operators, but that as the field contracts, the smaller number of remaining radiologists will enjoy persistent high wages in the form of obscolence rents? Also, sure.
Ultimately, humans are not quite a natural resource. The tortured metaphor may or may not hold in the future, but we should all be able to agree that it sure hasn’t happened in the past.
The Obsession with the Jevons
So why is the world—and everyone contributing to the AI bubble—so obsessed with Jevons’ Paradox?
Because it’s comforting.
Instead of worrying that the economic value of most humans is going to trend toward zero and that our entire society will have to contend with massive disruption, it’s far easier to believe that AI efficiency will unlock the magic of productivity. That somehow, this machine intelligence revolution will set us free to do our best, most magical, most human work.
A narrative where increased efficiency leads to increased demand soothes the uncertain soul. In that version of the story, the highest-skilled humans won’t be made obsolete—they’ll be unleashed.
I don’t know if that logic will hold in real life over the coming years or not. What I do know is that invoking it right now is a marketing fantasy.
To reiterate: I don’t know what will happen to radiology or to any other field. The future is uncertain, and I certainly don’t have a crystal ball.
What I do know is that all attempts to pretend radiology has already seen the fruits of AI and absorbed them—that AI is responsible for the current surge in demand for radiologist services—are lies.
But we can appreciate why this specific variation of the lie is being told:
In 2016, AI Godfather Geoffrey Hinton famously said:
People should stop training radiologists now—it’s just completely obvious within 5 years deep learning is going to do better than radiologists. It might be 10 years, but we’ve got plenty of radiologists already.
So the fact that we’re still here and in more demand than ever is supposed to be comforting to other humans.
Don’t fear AI! Even the radiologists are thriving!
I’m sure that in advance of an earnings call, a narrative where AI unlocks human potential is so much more compelling than one with a zero-sum game where the short-term economic value goes to tech companies but the long-term impact is to potentially destroy human enterprises and sink the economic value of previously high-training, high-skill, once-human tasks.
If we think further ahead, no one is going to pay for computer work the same as we did for comparable human work for a sustained period. The RVU system in healthcare already attempts to account for effort, liability, etc. We don’t get to hold even that one system’s current balance fixed while the world changes. Causes yield effects, and consequences themselves have consequences.
Anyone who imagines a perpetual money-printing machine generating revenues to match the humans they once replaced is naive, so maximum AI utopianism (and the value tied up in these companies) doesn’t envision that world. The devastating disruption fears may or may not be valid, but Huang and others in the tech world clearly feel compelled to address them.
It seems to me that Huang is trying to wave away generalized replacement fears by pretending that radiology is the canary in the coal mine and we’re still here, therefore rainbows and unicorns.
Maybe that “doomer” path to darkness is wrong, but that doesn’t mean radiology is the example to light the way.