Radiologist — tasks, one by one
The unit of analysis is the task, not the job title. Each one below carries its direction, whether the judgement rests on evidence or on platform inference, the reasoning, and what it does not establish.
Every task on this page#
Reading the routine study
Automating✓ Evidence-backedHigh-volume, pattern-clear work: screening mammograms, chest films, follow-up scans where the question is narrow and the answer is usually normal.
This task has the four properties that make anything a strong case for machine work: the input is an image, the output is a label, correctness is checkable against a later outcome, and the training data exists in enormous quantity because every study was already read and reported by someone. It is also the task the 2016 prediction was actually about, and on this task the prediction was directionally right even though its timeline and its conclusion were not.
Says nothing about how much of a radiologist's day this is, and the share differs enormously between a screening programme and a tertiary hospital. The two records attached here are a count of what regulators have authorised and a count of what training programmes have funded; neither is deployment. A cleared device is not a hospital running it, and a funded residency post is not a study being read — the first is a ceiling on what is permitted, the second a bet on demand years out. What would settle it: a health system's own report of what share of studies are machine-read before a person sees them.
Catching what nobody asked about
Still human-led≈ Platform inferenceThe scan was ordered for a back injury; the thing that matters is in the corner of the image and has nothing to do with the back.
A detection model is trained on the question that was asked. The incidental finding is by definition the answer to a question nobody asked, and it is a large part of what makes reading valuable rather than merely accurate. Improving a model on its own task does not move this, because it is not that task.
A judgement about the structure of the work, not a measurement. How often incidental findings change what happens to a patient is not quantified in a way that transfers, and the answer differs by modality and by population. It also does not claim people are good at this — missed incidentals are a known and studied failure.
Deciding whether to image at all
Being augmented≈ Platform inferenceProtocolling: whether this study answers the clinical question, which sequence, how much contrast, how much dose — and whether to say no.
Decision-support tools genuinely help here: appropriateness criteria are rule-shaped and a system can apply them consistently where a busy person will not. What does not transfer is the refusal — declining a study a referring clinician wants is a judgement with a cost that lands on a named person, and the value of the radiologist in that moment is precisely that they can be argued with.
Nothing here says how often radiologists actually protocol studies rather than rubber-stamping them, and in many settings that step has already been delegated or automated away for reasons unrelated to AI.
The report somebody acts on
Being augmented✓ Evidence-backedTurning what you saw into words a clinician will act on — including how much to hedge, and whether to recommend a follow-up that costs money and worry.
Drafting and structuring reports is well-suited to generation and is where most of the visible time saving is claimed. The part that does not move is the calibration: a sentence that hedges too much is useless and one that hedges too little causes an unnecessary operation, and where that line sits depends on this patient, this referrer and this health system.
Nothing public measures report-drafting time saved, and vendor claims about it are marketing from a party with a stake in the answer. What would settle it: a health system publishing its own before-and-after turnaround times.
Procedures done inside a person
Still human-led≈ Platform inferenceImage-guided biopsies, drains, embolisation — interventional work where the image is the guidance rather than the product.
Physical, irreversible, performed on a person who is present and can be harmed. Robotics in this area assists a hand rather than replacing one, and regulation attaches the act to a licensed individual. This is the half of radiology that the 2016 prediction did not address at all.
Interventional radiology is really a separate occupation with its own training path, and treating it as one task of this page understates how different it is. It also says nothing about volume, which is where the actual pressure on this work comes from.
Answering for the machine that read it
New task✓ Evidence-backedValidating a tool on your own population before switching it on, watching for drift after, and being the named person on a report the machine helped write.
Work that did not exist before these tools entered the reading room, and it lands here because the signature does. A model validated elsewhere is not validated here — performance moves with scanner, protocol and population — so somebody local has to check, and checking is a radiologist's skill applied to a machine instead of to a patient.
New work appearing is not new headcount, and in most departments this is absorbed by whoever is already there. What would settle it is a hospital publishing a post created for this and the hours attached to it.