Extracts from an article in Understanding AI with the title "AI isn't replacing radiologists" (https://www.worksinprogress.news/p/why-ai-isnt-replacing-radiologists):
Radiology accounts for the vast majority of AI medical devices
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.
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. First, while models beat humans on benchmarks, the standardized tests designed to measure AI performance, they struggle to replicate this performance in hospital conditions. Most tools can only diagnose abnormalities that are common in training data, and models often don’t work as well outside of their test conditions. Second, attempts to give models more tasks have run into legal hurdles: regulators and medical insurers so far are reluctant to approve or cover fully autonomous radiology models. Third, even when they do diagnose accurately, models replace only a small share of a radiologist’s job. Human radiologists spend a minority of their time on diagnostics and the majority on other activities, like talking to patients and fellow clinicians.
Artificial intelligence is rapidly spreading across the economy and society. But radiology shows us that it will not necessarily dominate every field in its first years of diffusion — at least until these common hurdles are overcome. Exploiting all of its benefits will involve adapting it to society, and society’s rules to it.
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The full article is well worth reading if you are interested in this field.
Chris Zielinski
Centre for Global Health, University of Winchester, UK and
President, World Association of Medical Editors (WAME)
Blogs; http://ziggytheblue.wordpress.com and http://ziggytheblue.tumblr.com
Publications: http://www.researchgate.net and https://winchester.academia.edu/ChrisZielinski/
HIFA Profile: Chris Zielinski held senior positions at the World Health Organization for 15 years, in Africa, WHOs Geneva Headquarters, and India, and earlier in other UN-system organizations working in writing, media, publishing, knowledge management, and intellectual property. He also spent three years as Chief Executive of the Authors Licensing and Collecting Society (looking after the intellectual property revenues of all UK authors and journalists). Chris was the founder of the ExtraMED project (Third World biomedical journals on CD-ROM), and managed the Gates Foundation-supported Health Information Centres project. At WHO he was appointed to the Ethical Review Committee, and was an originator of the African Health Observatory during his years in Brazzaville. With interests in the information, and computer ethics and bioethics, Chris has edited numerous books and journals and worked as a translator. Now working independently, Chris has recently finished writing a travel book called Afreekinout. Email: chris AT chriszielinski.com