Why AI isn't replacing radiologists: models underperform in hospital settings, AI use faces legal hurdles, and the job is much more than image recognition

For years, radiology has been the go-to example in conversations about AI and professional obsolescence. … Bluesky: Eric Knutson / @alwayscorrect : I think they are using predictive AI for that, not LLMs. From what I heard years ago, I thought that was actually helping a lot. But maybe the criteria for it “working” is when they can fire all the radiologists @nanlinear : it was never about ‘replacing’ radiologists, they don't just read, they are skilled in imaging as well... ...it is another layer of checks that would minimise chance of misdiagnosis considering the high stakes! Mastodon: Miguel Afonso Caetano / @remixtures@tldr.nettime.org : “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’. … Forums: Hacker News : Demand for human radiologists is at an all-time high r/BetterOffline : AI isn't replacing radiologists Msmash / Slashdot : AI Isn't Replacing Radiologists

The Works in Progress Newsletter 2025-09-26

Context & Ripple Effects

Radiology has long been a prominent test case for clinical AI: prior coverage documented tumor-detection tools and noted that radiology accounts for most FDA-cleared medical AI software. Yet the more recent operational example was Mayo Clinic’s use of AI to augment radiologists, not eliminate them.

This report adds hospital-setting performance and legal accountability to an older concern that medical data can produce misleading AI results outside idealized conditions. It reframes radiology AI as a workflow and clinical-governance problem rather than a standalone image-classification contest.

First-order effects

Radiologists remain the accountable clinical decision-makers where models underperform in hospital deployment and legal constraints limit autonomous use.
Hospitals and AI suppliers must treat image-analysis systems as decision-support tools, with validation and human review built into deployment rather than assuming benchmark performance transfers to care settings.

Second-order effects

Vendors face pressure to demonstrate utility in local clinical workflows, not just image-recognition accuracy; that shifts buying criteria toward measurable time savings, reliability, and integration with radiologist practice.
Demand for augmentation tools can persist without replacing specialists, consistent with the earlier rollout of tumor-detection software and the large share of medical AI focused on radiology.

Third-order effects

If this pattern holds, AI adoption in regulated professions will automate bounded tasks while preserving human responsibility for judgment, exceptions, and liability-sensitive decisions.
The durable competitive advantage may move from the model alone to clinical validation, workflow integration, and evidence that a tool lowers the cost per useful task without worsening diagnostic incentives.

The trend: AI in high-stakes clinical work is moving from replacement rhetoric toward human-supervised, workflow-specific automation that must prove value in real deployments.

Discussion

@tunguz Bojan Tunguz on x
No one who has ever worked with any serious problem involving biological, and especially medical, data would have ever made such a sweeping claim.
@positivfuturist Andy on x
“Jevons paradox: if radiologists are sped up via AI as a tool, a lot more demand shows up.” This. But for the whole economy.
@omooretweets Olivia Moore on x
I spend a lot of time w/ cos adopting AI. By and large, AI isn't “taking jobs.” Every job is a collection of tasks - many of which AI can't do now (and may never do!) AI frees humans to do tasks where they're needed.
@pmarca Marc Andreessen on x
💯 Tasks are not jobs.
@yacinemtb Kache on x
paradoxically, coding LLMs have increased the amount of work that i have. every new release, the opportunity cost of my time goes up. the work i did in the past month would simply have not been worth doing without coding assistance
@levie Aaron Levie on x
The reason that AI isn't going to wipe out jobs in the way that some predict is that we consistently make the mistake of thinking that when we make something more efficient, you need commensurately less supply. It turns out that in a significant number of fields, better [image]
@sriramk Sriram Krishnan on x
Great post on why AI will often be augmenting humans in their jobs, not replacing them.
@afinetheorem Kevin A. Bryan on x
AI economists and AI researchers: this is *excellent*. Details below, but as I feel like I've said in every talk on this topic since my slides said “GPT-2”, 1) AI technical capabilities are better and improving quicker than you think, 2) impact on economy *much* slower. 1/13 [ima…
@sundeep Sunny Madra on x
Great post. Tldr; keep calm, carry on building with AI. It's better for everyone.
@deenamousa Deena Mousa on x
In 2016 Geoffrey Hinton said “we should stop training radiologists now” since AI would soon be better at their jobs. He was right: models have outperformed radiologists on benchmarks for ~a decade. Yet radiology jobs are at record highs, with an average salary of $520k. Why? [ima…
@karpathy Andrej Karpathy on x
“AI isn't replacing radiologists” good article Expectation: rapid progress in image recognition AI will delete radiology jobs (e.g. as famously predicted by Geoff Hinton now almost a decade ago). Reality: radiology is doing great and is growing. There are a lot of imo naive
@alwayscorrect Eric Knutson on bluesky
I think they are using predictive AI for that, not LLMs. From what I heard years ago, I thought that was actually helping a lot. But maybe the criteria for it “working” is when they can fire all the radiologists
@nanlinear @nanlinear on bluesky
it was never about ‘replacing’ radiologists, they don't just read, they are skilled in imaging as well... ...it is another layer of checks that would minimise chance of misdiagnosis considering the high stakes!
r/BetterOffline r on reddit
AI isn't replacing radiologists

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