AI May Expand Healthcare Workforce, Not Replace It

Economic principles point to workforce growth

Artificial intelligence tools in healthcare may ultimately create more jobs for clinicians rather than eliminate them, according to a new analysis that applies economic theory and historical precedent to the automation debate.

Dr. Dhruv Khullar, an associate professor of population health sciences at Weill Cornell Medicine and hospitalist at NewYork-Presbyterian/Weill Cornell Medical Center, challenges the widespread assumption that AI will shrink the clinical workforce. Writing in The New England Journal of Medicine, he argues that while some roles will certainly change, several economic mechanisms suggest the opposite outcome is more likely.

Why it matters

Healthcare leaders planning workforce strategies need frameworks beyond simple automation-equals-job-loss thinking. Understanding how efficiency gains historically drive demand growth—rather than headcount reduction—helps organizations prepare for expansion rather than contraction, particularly in specialties like radiology, primary care, pathology and psychiatry that face the highest AI exposure.

Historical patterns suggest expansion

Khullar points to cataract surgery and joint replacement as instructive examples. Both procedures became dramatically more efficient through technological advances that reduced clinical effort, shortened recovery times, and improved safety. The result wasn't fewer surgeons—it was more patients receiving care they previously couldn't access.

This pattern reflects Jevons paradox, where efficiency improvements increase rather than decrease total resource consumption. If AI systems are priced near marginal cost and reduce the overall expense of delivering care, similar dynamics could unfold across healthcare services.

The fallacy of fixed work

Expectations of AI-driven job losses rest partly on what economists call the "lump of labor" fallacy—the mistaken belief that there's a fixed amount of work to be done. Khullar argues that both the type and volume of work evolve in response to technological capability.

AI may enable clinicians to prevent or treat conditions in ways not currently possible, creating demand for new professional capabilities, treatment modalities, and specializations that don't yet exist.

Tasks versus jobs

Automating individual tasks doesn't necessarily automate entire jobs. Khullar invokes "O-ring theory," named after the faulty Space Shuttle Challenger component, which holds that a single failure at any step in a complex process can undermine the entire outcome.

Delivering high-quality care involves numerous interconnected steps—interpreting results, developing treatment plans, negotiating with patients. High-stakes medical situations will continue requiring clinical supervision for safety and trust. When AI handles some tasks, the value of remaining human tasks may actually increase.

Planning for growth

While Khullar acknowledges that clinical roles will transform and some job types could disappear, his analysis suggests healthcare organizations should prepare for workforce expansion rather than contraction. The combination of increased care accessibility, new treatment possibilities, and the irreducible need for human judgment in complex medical decision-making points toward more clinicians working in different ways.

The analysis was first reported by Weill Cornell Medicine and published September 12 in The New England Journal of Medicine.