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NewsMaterial4 min readDisconfirmAI Editorial

Jason Wei: Radiologists Will Not Be Replaced by AI Anytime Soon, Because Demand Will Expand Alongside AI

In this blog post, Jason Wei uses radiology as a case study to argue that AI will first serve as a triage tool, while elastic demand, data bottlenecks for rare diseases, and healthcare system friction will delay full automation.

2026-10-20 03:23 GMT+8

Over the past few years, radiology has often been cited as a prime example of a job that AI would replace. In 2016, Geoff Hinton asserted that people should stop training radiologists, claiming that deep learning would outperform them within five years. Jason Wei offers a more cautious judgment in this blog post: while radiology will change over the next decade, radiologists will not be quickly automated away. His core argument is not that AI lacks capability, but rather that the structure of radiology work will first position AI as a triage tool, rather than replacing all image-reading tasks at once.

The first mechanism is the "jagged edge of intelligence." Jason Wei points out that language models excel in verifiable, data-rich common tasks but remain unstable in less common ones. Radiology is not a single task but an extremely multi-task domain: X-rays alone may involve at least a hundred diagnosable conditions, while CT and MRI involve thousands. AI will first master common, easily detectable diseases and will not automatically become proficient in rare diseases simply because the overall model improves. He argues that moving from a reliable triage tool to full automation will take at least three to five years even in the best-case scenario, because training AI on edge cases will be bottlenecked by data collection for rare diseases, and radiology is currently not a priority focus for frontier model laboratories.

The second mechanism is elastic demand, also known as the "Jevons paradox." Jason Wei considers this the most underestimated phenomenon in discussions about AI automation. He cites his own use of Claude Code as an example: when coding costs dropped, he asked the model to write a roughly 3,000-line HTML viewer to visualize every reinforcement learning run he performed. This illustrates that software work did not see reduced demand due to stronger AI; instead, more tools that were previously deemed not worth building became feasible. The same could happen in radiology: if the cost and latency of reading MRIs approach zero, doctors may order more scans preemptively to aid diagnosis. His illustrative estimate is that AI tools might allow radiologists to read only 20% of incoming scans, but the volume of incoming scans could increase fivefold. Many developing countries have fewer than one radiologist per million people and may also send significantly more imaging for interpretation.

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Jason Wei further extends this demand expansion into the longer term. If image interpretation adds almost no waiting time, medical device companies will have incentives to manufacture cheaper, higher-frequency-use MRI machines, potentially even using them during surgeries. He also proposes a hypothetical: if a whole-body MRI cost only $100, he would prefer to get one monthly rather than biennially. This is not an already occurring fact, but an example used to demonstrate elastic demand. Following this logic, after radiology prices drop ten or a hundredfold, the number of preventive checks could rise substantially, meaning radiology work will not disappear just because individual image readings are automated.

The third mechanism is that medicine itself changes. Even if a model emerged today that could outperform the world's best radiologists, radiology work would not end. New treatment protocols may require doctors to read images in ways they are not currently accustomed to, and new scanning equipment may reveal diseases that cannot currently be diagnosed through radiology alone. Jason Wei believes some radiologists will shift from reading scans to frontier research, translating new knowledge into updated models. Meanwhile, society will demand more from AI, such as using images to predict future disease probabilities, enabling cancer detection months earlier than currently possible. If this predictive capability creates a flywheel effect, it will further increase demand for radiology and AI systems.

Healthcare systems will also slow down complete replacement. Jason Wei notes that medical systems typically adopt new technologies slower than tech companies, face more regulation, have more complex legacy infrastructure, and must continuously serve patients. Data access is also restricted by privacy and ethical review processes. These factors do not prove that radiologists will never be replaced, but they indicate that replacement will not be an instantaneous switch triggered solely by model metrics surpassing those of doctors.

The most useful aspect of this blog post is breaking down the emotional question of "Will AI replace radiologists?" into several checkable conditions: Is the task highly multi-task oriented? Are there sufficient data for rare cases? Will demand expand after unit costs drop? Can the medical system absorb new tools? And will doctor work shift toward research and model updates? Jason Wei concludes that radiology will thrive rather than simply vanish; he cites programming as a similar example, arguing that when domain changes drastically but demand and quality ceilings rise simultaneously, professional work will still exist.

The boundaries of this judgment are also clear. It is Jason Wei's prediction and mechanistic explanation, not verified employment data or healthcare market results. If the rare disease data bottleneck is quickly resolved, if model generalization exceeds his estimates, or if healthcare payment reforms cause more scans to not translate into radiologist positions, radiology work could still decrease significantly. Conversely, asserting that radiologists will soon lose their jobs solely because models improve on common imaging tasks ignores two key mechanisms: triage precedes full automation, and elastic demand expands workload.

Sources

  • jasonwei.netJason Wei↗
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