---
title: "Ten years after AI was said to end radiology the salary is 571000 dollars"
description: "In 2016 Geoffrey Hinton said it was completely obvious we should stop training radiologists. The average US radiologist salary was $571,000 in 2025, the profession has grown about 10 percent in a decade, and thousands of posts sit unfilled."
category: "Companies"
category_url: https://boursel.com/category/companies
author: "Marcus Feldman"
published: 2026-07-19T13:56:00.000Z
updated: 2026-07-19T13:56:00.000Z
canonical: https://boursel.com/article/ten-years-after-ai-was-said-to-end-radiology-the-salary-is-571000-dollars
tags: ["artificial-intelligence", "labor-market", "healthcare", "automation", "forecasting"]
---
# Ten years after AI was said to end radiology the salary is 571000 dollars

In 2016 Geoffrey Hinton said it was completely obvious we should stop training radiologists. The average US radiologist salary was $571,000 in 2025, the profession has grown about 10 percent in a decade, and thousands of posts sit unfilled.

In 2016, at a machine learning conference in Toronto, Geoffrey Hinton told the
audience that hospitals should stop training radiologists. It was "completely
obvious," he said, that within five years, or ten at most, AI would read medical
images better than people. "If you work as a radiologist, you're like the coyote
that's already over the edge of the cliff but hasn't yet looked down."

A decade on, the average US radiologist salary was $571,000 in 2025, [according
to
Fortune](https://fortune.com/article/ai-godfather-radiologists-obsolete-salaries-up-to-571k-demand-growing/).
The number of active radiologists in the United States has grown by about 10
percent over that period.

"We actually have a huge shortage of radiologists. So the exact opposite of this
prediction has happened," Christoph Herpfer, an economist at the University of
Virginia's Darden School of Business who studies physician labour markets, told
the magazine. There were 7,469 active radiology job postings as of May, of which
1,470 had been open more than 60 days.

## Being fair to Hinton

Hinton has since narrowed the claim, clarifying last year that he was speaking
purely about image analysis and saying that human radiologists will work
alongside AI to become more effective. That is a meaningful retreat from "stop
training radiologists," and it should be recorded.

It is also worth separating two questions that get conflated. Was Hinton wrong
that machines would become very good at reading images? Largely no; AI image
analysis has advanced enormously. Was he wrong that this would eliminate the
profession? Yes, and the gap between those two answers is the whole lesson.

## Why the prediction failed

Three things explain it, and none is specific to medicine.

The first is that a job is not a task. Reading scans is the most visible part of
radiology and far from all of it. Radiologists perform image-guided procedures,
advise other physicians on which test to order, communicate findings to patients
and colleagues, and carry legal and professional responsibility for the
interpretation. Automating the most legible component of a role leaves the rest
in place.

The second is that demand moved. Imaging volumes have risen as populations aged
and as insurance coverage expanded, and cheaper, faster interpretation tends to
generate more scanning rather than less. When automation reduces the cost of an
activity, consumption of it frequently rises, which can more than offset the
labour saved per unit. Radiology has been a clean example.

The third is accountability. A system that is highly accurate still produces
cases where it is wrong, and someone has to be answerable for those. That
requirement does not disappear with better models; it concentrates the human
role on exceptions and on responsibility, which is harder to automate than
throughput.

## What this does and does not tell us

The honest caveat is that being early is not the same as being wrong, and
nothing here proves AI will not eventually displace radiologists. Hinton's
timeframe has passed, but timeframes are the easiest part of a forecast to get
wrong while the direction is right.

Nor is the wider labour picture reassuring by default. Snap and Block have cited
AI in cutting thousands of jobs. Anthropic's chief executive Dario Amodei has
warned AI could eliminate half of entry-level white-collar jobs within five
years, though he has more recently argued it may also transform and expand
certain kinds of work rather than simply replace them.

The useful generalisation is narrower than either the doom or the dismissal.
Automation has historically removed tasks faster than it has removed
occupations, and the occupations most exposed are those where the automatable
tasks constitute most of the job and where demand is fixed. Radiology failed
both tests: the readable-image portion was a minority of the work, and demand
for imaging was nowhere near saturated.

For anyone assessing which jobs are genuinely at risk, that is a more useful
screen than asking whether a model can do the headline task. The questions are
what fraction of the role the task represents, whether cheaper output expands
the market, and who carries the liability when it goes wrong.

## Sources

- [A decade after the 'Godfather of AI' said radiologists were obsolete, their salaries are up to $571K and demand is growing fast](https://fortune.com/article/ai-godfather-radiologists-obsolete-salaries-up-to-571k-demand-growing/)