In many practices and especially in academia, important but burdensome tasks are either treated like hot potatoes or flow downhill to the most junior faculty. There are several strategies for distributing important but non-measurable or non-promotable tasks:

• random assignment
• rotating schedules
• clear benefits like compensatory time off
• automatic cycling/off periods (e.g., do a task for one year, then off for four years)

Avoid asking for volunteers in some situations, particularly for tasks that have little upward mobility, little chance to shine, and where performance is perhaps less important or easy to measure. Otherwise, you get the same people-pleasers doing everything and burning out. Even if they’re not burning out, their time is taken up (perhaps by doing the wrong activities as acts of service).

Alternatively, if a task is important enough to get done, consider ways in which it can be part of someone’s job description. This may be more practical when something undesirable is paired with something desirable, like remote schedules with a more challenging case mix.

Another possibility is putting your money where your mouth is. My practice has a small component of bonus compensation tied to accumulating enough brownie points for doing various tasks like tumor boards, recruiting activities, etc. The reality in our group is that we are large enough and the tasks varied enough that the minimum threshold to qualify is laughably small, and there aren’t measurable benefits to accumulating more than you need. It ensures a floor, probably relying more on the principle loss aversion than the actual money at stake.

You can give people effort RVUs for doing those tasks, but in this market, anything that gives you credit towards a full day’s work that detracts from reading cases can also contribute to overall staffing problems. In other clinical fields, it just pushes more work into the evening/weekends (and eats up precious academic/admin time if in the mix). In small groups where such activities may be distributed evenly, nothing may need to happen. In larger practices where most members will do nothing except interpret scans, stipends are an option, particularly for larger tasks like division directorship.

Bean counting is fine to an extent, and some activities like multidisciplinary conferences are easy to count. But many important tasks are challenging to quantify and may even backfire when attempted. The classic daycare fine experiment added a financial penalty when parents arrived late to pick up their infants, which replaced a powerful social norm with a market norm (swapping guilt for a few dollars). Sometimes, something people would do for free is devalued when a sticker price is put on it that is laughably small.

Consider preventing excess accumulation/consolidation: if someone is going to do something more, the default should be for that person to do something less as well. It’s not just for their benefit. Our institutions are fragile when they rely on individuals too much for any reason.

(On a related note, documentation is key for onboarding and transferring. Truly useful internal documents that are up to date are exceedingly rare.)

Distinguish between areas where someone is specifically important—where the specific representation matters—versus when someone is a warm body. Not all extra work is the same, and not all additional roles are interchangeable or easy to replace. The ones that leave a real hole need to be valued more, even if that comes mostly in the form of consistent gratitude.

If you think someone special knows just how much they are respected and appreciated, they don’t. I promise.

Most Content is Boring

Most current articles about AI and radiology work don’t help us much with regard to the questions people are constantly asking about predicting the future. Publish or perish is fine, but I’ll admit I also don’t really care how many ways we can say these early tools are sorta maybe helpful sometimes for some people but are also super brittle and unreliable and also the most important thing is that they don’t make things worse, particularly due to bad integration with a legacy workflows. How to incorporate something only borderline useful into a workflow you probably don’t control just isn’t very interesting, and people keep dropping predictions about what things will look like years (or even decades!) in the future based on what we’ve seen so far.

Extrapolating linear growth when we have exponential compute isn’t helpful. But imagining a post-work techno-utopia vs dystopia is also not actionable for anyone. We can skip the hand-wringing and just agree that everything currently available isn’t good enough to change the world but that maybe one day it will be.

We live in a complicated world where many of what we perceive as challenging problems are computationally straightforward (e.g. cumbersome long calculations, playing chess) and other things that can become automatic to most humans are computationally intense (as it turns out, driving a car, though it’s getting better!). Corner cases and uncertainty are, in some sense, the whole game when it comes to automation. Medicine—with its treatment algorithms designed for squishy humans with limited narrative skills and inherently incomplete information—is a combination of both.

Lots of medicine is straightforward and subject to guild protectionism, but some of it is genuinely hard. And sometimes the difference between those two situations is only knowable after the fact. If nothing else, ambiguity is hard, and how much uncertainty and risk to accept has never been codified. For example, many current AI tools are designed for a high negative predictive value and are therefore overly sensitive. That’s fine for a tool to help a human not make a mistake, but that still can lead to cry-wolf problems and certainly wouldn’t work for an autonomous machine.

Balancing uncertainty and service expectations is no joke for humans or machines or the combination.

Even Good Predictions about Important Things Are Challenging to Act On

As I’ve said before, I don’t think the reality of impending change means that we can meaningfully predict the contours of that change or should even try to plan for its eventuality in ways that would be destabilizing in the intervening time. What Hinton got wrong is that predictions about replacement are dangerous because, until replacement actually occurs and/or demand shifts, we need to maintain the status quo. There’s no way to know if any field (whether primary care or even surgery) will be augmented or replaced over an effectively infinite timeline, or if the contours of the work will change and if so to what extent and how fast, and therefore what the correct number of people we need long-term such that we could preemptively adjust the pipeline of training to help us land at the optimal amount of people for every kind of job. It’s not going to happen.

For long training pathways like most physician careers, trying to pretend we can is insanity. Central planning in that fashion almost never works because there are far too many unknowns. What we need to do is continue training people for the world we live in with a plan, desire, and ability to adjust our workforce over time as the world changes. We need less planning and more flexibility. Less preciousness about how we work and more orientation on the desired outcomes.

Until the exact day a computer replaces a human, you need that human. Trying to predict a societal need years in the future is a fool’s errand. We haven’t been able to fill the halls of medicine  “correctly” over the past century; why would we be better at doing it now?

The Breast Scenario

Let’s just posit a very narrow scenario to see how futile the situation is. Let’s magically say AI for screening mammography becomes incorporated into major vendors at some point in the next X years, allowing for automated reports, even for callbacks. A computer report and a human report would be indistinguishable, especially since BI-RADS facilitates complete automation of reporting language and organization. (I’m also picking breast imaging for this thought experiment because it’s currently very much in demand, tends to be narrowly subspecialized, and has relatively unique current job offerings.)

Let’s also say that the algorithm performance and regulations are such that, for now, those exams are still read by a human being as well—at least in the short term. Guess what the incremental efficiency gain will be? How many screeners can someone read in that setting? The answer, of course, is that it varies.

Automation bias is going to hit fast, so it’s not clear if someone will be able to read 10-20% more, twice as many, three times as many, or some other multiple. Whatever the number is, the first question becomes: how does that affect breast imaging staffing? Does it change the premium that breast imagers can currently command because of the high volume of high-paying screening exams, particularly those with 3D tomosynthesis? How many nefarious rubber-stampers do we need to change the competitive landscape? Think of how fast turnaround times can be! This of course will happen on the backdrop of continually increasing imaging volumes and technological development.

Okay, then what happens to mammography reimbursement? The RVUs will inevitably change if the effort to do the work changes. How fast will that happen, and how drastic will the change be? The RVU system is a zero-sum game, and undoubtedly, there will be a lot of fighting about any changes to radiology reimbursement that occur thanks to AI tools—but they will occur. Tomosythesis is well-paid and already in crosshairs.

How would the subspecialty handle a shifting balance of non-procedural and procedural work, or remote vs on-site work?

Then, how would the job market respond? Would we see similar jobs with less pay? Or would we see a chilling of the growing lifestyle niche of 4-day workweeks with no evenings or weekends? Would we just be able to better staff growing volumes? When I finished training less than a decade ago, it was common for breast imagers to take general call. It would seem that’s pretty rare now. Medicine, like politics, is local. Of course, breast imaging isn’t operating in a vacuum; this is all taking place within an even larger lifestyle teleradiology trend. But variables don’t play nice and isolate themselves: everything is always changing all the time.

What is the timescale for those changes, and how piecemeal would they be? What are the second and third-order downstream consequences of even a single shift in radiology productivity? What about fully automated DEXA? What about prepopulated ultrasound measurements? Natural language draft reports for other modalities. We don’t even know. But the answer isn’t simply that we need fewer breast imagers when there are currently not enough, just because one day we might.

Medico-legally, how will we as a society handle mistakes made by machines? Or, with a human checker, how will we weigh a change made by a human versus a mistake made by a human coupled with a machine?

How will we feel about a mistake where the AI misses something and the human doesn’t catch it–or doesn’t fix it–versus a situation where the AI calls something and the human pulls it back, even when the AI is right? How will we handle type 1 versus type 2 errors? Will companies self-insure for autonomous work as the dominant strategy, or will we see protectionist legislation about a variety of key human tasks?

We don’t know, long-term, whether there are edge cases where humans are more or less important. But we also don’t know, even short-term, where the contours of those skills will reside. Maybe we’re entering a world where the ED would be a better place if a computer did most of the interviewing and initial workup for stable patients.

We are amidst a jagged frontier where AI is good at some things and bad at others, and we know there is a lag between capacity, implementation, legislation, and regulation. Planning is always a challenge; useful prediction is basically impossible.

So, instead of predicting exactly what will happen, we need to acknowledge the broad potential possibilities that could occur and plan a system to deal with them when/if they do occur, as opposed to trying to nail the blind landing.

We All Just Do Stuff Anyway

I don’t want to call out any behavior as irrational, but we should acknowledge upfront the human tendency to just do whatever we want to do and then create post hoc rationales + explanations + narratives to justify and explain our behavior after the fact. Much of this is unconscious and may be a clever trick of our mushy brain psychology, but that doesn’t make it any less real. Try to be honest with yourself and see why you feel the way you do about just about anything and if the reasons you give yourself actually mesh with reality. Write it down, or you’ll cheat (it’s our nature).

It’s like a medical student saying they like “technology” and “problem solving” and “people coming to the reading room for answers” for why they picked radiology when it was really that it seemed relaxing and quiet with good vibes and plenty of coffee and that people at least respected the radiologists’ intelligence and oh yeah they heard they were paid well.

Over the past decade or so, psychiatry has gone from very uncompetitive to pretty competitive. Is it because mental health is important and less stigmatized? Surely that’s part of it. But maybe also because cash-based private practice in a high-demand field (potentially even done via telehealth) sounds like a nice quality of life and a rare way to practice medicine on your own terms. We always seek single explanations thanks to our bias for narrative, but that doesn’t mean that the compelling storytelling is any more reflective of reality.

The examples of failed central planning and the reliance on narrative are legion.

To reiterate my point: No single person or entity has the answer.

So—

Yes, we need all varieties of doctor now, and we need to keep training all of them until we don’t—if that ever occurs.

Will a job like radiology or even primary care pay the same 10 years from now as it does today, or 20 years from now? That will depend on how much value the human provides within the system, how much work they do, how hard or complex that work is, and the supply/demand balance of the available workforce. Volatility and all kinds of non-AI factors already lead to lots of unpredictability.

There may be golden years of excess income for scalable jobs like radiology as these changes first take place. If senior engineers can use AI to do more projects without hiring as many junior coders, it results in rising incomes for those on top and a rug-pull for those entering the workforce.

Both medicine and society need ongoing, never-ending conversations in order to work on creating adaptable systems. Instead of placing bets, the acronym organizations need to set stakeholders up for success across the full spectrum from an AI-nothingburger to AI-work-is-optional.