What Formula 1 Teaches Medicine and Healthcare About Artificial Intelligence

“In that moment. I’m flying…”                                       
Sonny Hayes, F1 driver in the movie F1, describing the best feeling while driving when man and car becomes “one”
            

Formula 1 is the most instrumented workplace on Earth: a single car streams over a million data points per second (yes, per second) during a race and generates roughly 400 gigabytes per race weekend, feeding AI models that run millions of strategy simulations. And yet the F1 sport is in a bit of open revolt currently, not against the data being wrong, but against what the data has done to the work.

The revolt has two strands, and each maps precisely onto medicine and healthcare:

First, expertise inversion. AI-driven telemetry now tells a driver exactly where he loses time in a corner and suggests the theoretically optimal line. Junior drivers love it as it compresses years of trial-and-error into months. Veterans like it less. When engineers radio a five-time world champion mid-lap to change a setting or save his brakes, the message he hears is not necessarily valuable information; it is perhaps a borderline insult to the very skill that made him an F1 champion. Sound familiar? The resident embraces decision support while sometimes the seasoned attending experiences it as a referendum on thirty years or more of clinical gestalt. Adoption resistance is not Luddism; it is perhaps professional identity.

Second, loss of agency. Under the 2026 F1 regulations, energy-deployment software has become so dominant that drivers describe qualifying as three-dimensional chess: push harder in the wrong corner and the software quietly robs you of power later in the lap. The veteran F1 driver Carlos Sainz's verdict: “the more you pushed, the slower you went” could be stitched onto the sleeve of every physician who has optimized to the EHR instead of the patient. Max Verstappen, another seasoned F1 driver, calls the new cars “Formula E on steroids.” The complaint is not that the machine is inaccurate. It is that the expert has become the executor of machine-scheduled behavior or Doctorow's “reverse centaur”, at 200 miles per hour.

 

The synthesis comes from the pit wall itself. F1 strategist Ruth Buscombe observes that every so-called gut call in racing is really a summation of numbers; but the deployment of the decision, not panicking, communicating clearly, acting at the right moment- these all remain irreducibly human. The model maintains situational awareness no person could, but the driver still makes the final call. 

The takeaway: F1 is not going to resolve the tension by simply removing the data or marginalizing the driver, it has to reconcile it by redesigning the work so the machine watches, but the human decides. Healthcare has not yet made that work synergy transition. That is the challenge that lies ahead: to keep clinicians in synergy with AI in delivering the best care for our patients while maintaining the highest joy for ourselves.