AI isn't replacing radiologists

by Works In Progress and Deena Mousa
Sept. 25, 2025

CheXNet can detect pneumonia with greater accuracy than a panel of board-certified radiologists. It is an AI model released in 2017, trained on more than 100,000 chest X-rays. It is fast, free, and can run on a single consumer-grade GPU. A hospital can use it to classify a new scan in under a second.
Since then, companies like Annalise.ai, Lunit, Aidoc, and Qure.ai have released models that can detect hundreds of diseases across multiple types of scans with greater accuracy and speed than human radiologists in benchmark tests. Some products can reorder radiologist worklists to prioritize critical cases, suggest next steps for care teams, or generate structured draft reports that fit into hospital record systems. A few, like IDx-DR, are even cleared to operate without a physician reading the image at all. In total, there are over 700 FDA-cleared radiology models, which account for roughly three-quarters of all medical AI devices.

Radiology is a field optimized for human replacement, where digital inputs, pattern recognition tasks, and clear benchmarks predominate. In 2016, Geoffrey Hinton – computer scientist and Turing Award winner – declared that ‘people should stop training radiologists now’. If the most extreme predictions about the effect of AI on employment and wages were true, then radiology should be the canary in the coal mine.
But demand for human labor is higher than ever. In 2025, American diagnostic radiology residency programs offered a record 1,208 positions across all radiology specialties, a four percent increase from 2024, and the field’s vacancy rates are at all-time highs. In 2025, radiology was the second-highest-paid medical specialty in the country, with an average income of $520,000, over 48 percent higher than the average salary in 2015.

Three things explain this.

Continued https://open.substack.com/pub/worksinprogress/p/why-ai-isnt-replacing-radiologists