Primul spital pediatric din România care implementează inteligența artificială în radiologie

Across the United States, physicians at orthopaedic urgent care clinics routinely review the prior day’s X-rays. Most of the time, these reviews validate what has already been identified by the advanced practice provider (APP). Some identify a discrepancy; a small number of those discrepancies involve a patient who has gone home, received provisional reassurance, and now requires a phone call to bring them back.

In the U.S., the number of medical studies that radiologists receive on an annual basis has nearly doubled, from 14,900 studies per year in 2008 to 26,457 studies per year in 2018. Yet, there was only a 13% increase in new radiologists entering the radiology field over the same period.¹ As of the end of 2024, the quarter of radiologists read 30.6% more studies per day than they did in 2018, and they performed 19.7% more clinical shifts each quarter than they did in 2018.² This radiology workforce shortage is occurring at the same time as radiology imaging volume growth, which is one reason AI for X-rays has become a practical workflow topic rather than a theoretical discussion.

Radiology departments are under more pressure than they have ever been. Imaging volumes continue to grow year over year while the radiologist workforce struggles to keep pace, a gap that will widen further through 2030 and is identified as a core driver of diagnostic risk in emergency settings.⁶

Missed fractures on X-rays sit at the intersection of these pressures, a clinical problem that is well-documented, measurably costly, but increasingly addressable.¹ ²

This article examines why missed fractures happen, when they are most likely to occur, and how AI detection tools are beginning to close the gap without displacing the radiologists who remain crucial to the diagnostic process.