Medical dosimetrists: AI exposure and career outlook
Medical dosimetrists (SOC 29-2036) sit at the 51st percentile for measured AI exposure among the 342 occupations tracked here, measured from a composite of Microsoft Research and Anthropic Economic Index telemetry. An estimated 25% of tasks are already automated and 52% are being reshaped rather than replaced — both modelled figures, not direct measurements. Of the Claude usage observed in this occupation, 32% follows an automation pattern and 68% an augmentation pattern, which places it in the Orchestrator opportunity group: AI reaches deep into this work and amplifies it. The leverage goes to whoever directs it, and that can be you. In Canada the role maps to NOC 32121 (Medical radiation technologists), and ESDC's COPS 2024–2033 outlook for that unit group is strong risk of shortage.
Figures last updated 2026-06. Every number on this page is labelled measured or modelled; where a source has no coverage for this occupation, it says so rather than showing a zero.
Key facts
- SOC code
- 29-2036
- Canada NOC 2021
- 32121
- TEER level
- 2 — College / Apprenticeship 2+ yrs
- COPS outlook
- Strong risk of Shortage
What the measurements actually say
One of three independent studies measure AI exposure for this occupation directly; the others report no coverage for it. The two percentages below them are ours, modelled from those measurements.
| Metric | Value | Provenance |
|---|---|---|
| AI applicability | 16% | Measured — Microsoft Research, from 200,000 Copilot conversations classified against O*NET work activities. |
| Observed AI usage | Not available | Not covered by Anthropic's occupation-level dataset. |
| Academic AI exposure | Not available | Not covered by the Felten AIOE table. |
| Automation-pattern usage | 32% | Measured — the share of observed Claude usage in this occupation where the task is handed over rather than iterated on (Anthropic Economic Index). |
| Augmentation-pattern usage | 68% | Measured — the complement of the row above; the two sum to 100% by construction. This is the pattern where the person stays in the loop. |
| Estimated task automation (modelled) | 25% | Modelled from this occupation's measured telemetry composite, mapped through anchors calibrated to Anthropic's published aggregate findings. Our estimate, not per-occupation telemetry. |
| Estimated task reshaping (modelled) | 52% | Share of tasks where AI acts as co-pilot rather than replacement. Modelled from the measured telemetry composite, calibrated to BCG's published aggregate reshaping rates. Our estimate. |
AI is transforming how this work is done. Professionals who adapt their workflows will thrive; those who don't face increasing competition.
In Canada: NOC 32121
Medical dosimetrists map to NOC 32121 — Medical radiation technologists at TEER 2 (College / Apprenticeship 2+ yrs). ESDC's COPS 2024–2033 projection for this unit group is Strong risk of Shortage.
Mapped via Statistics Canada's official SOC 2018 → NOC 2016 → NOC 2021 correspondence tables. TEER is the second digit of the official NOC code, so it cannot disagree with it.
What this job actually involves
These are the O*NET task statements for Medical dosimetrists— the work itself, as reported by people doing the job — grouped by the work activity each task belongs to. Showing 12 of 19, core tasks first.
The exposure figures above are for the occupation as a whole. We do not have a per-task measurement: no published dataset tells us which of these specific tasks AI is doing. Read this as what the job is made of, not as a ranking of what is at risk.
1 of these 12statements do carry one measured signal: Anthropic publishes, per O*NET task, how much of the observed Claude usage on it looked like automation rather than iteration. Those are marked below. It still says how people use AI on that task, not how much of it AI can do — and the unmarked statements carry nothing rather than inheriting the occupation's figure.
Microsoft's applicability score was itself produced by classifying Copilot conversations against the O*NET work-activity catalogue — the same catalogue these groups come from. The published file gives one number per occupation, not one per activity, so we can tell you that 16% of this occupation's work activities are covered by observed AI usage, but not which ones.
Controlling Machines and Processes · 3 tasks
Identify and outline bodily structures, using imaging procedures, such as x-ray, magnetic resonance imaging, computed tomography, or positron emission tomography.
Core task · Importance 4.8/5 (O*NET)
Supervise or perform simulations for tumor localizations, using imaging methods such as magnetic resonance imaging, computed tomography, or positron emission tomography scans.
Core task · Importance 4.6/5 (O*NET)
Create and transfer reference images and localization markers for treatment delivery, using image-guided radiation therapy.
Core task · Importance 4.5/5 (O*NET)
Analyzing Data or Information · 2 tasks
Calculate the delivery of radiation treatment, such as the amount or extent of radiation per session, based on the prescribed course of radiation therapy.
Core task · Importance 4.8/5 (O*NET)
Calculate, or verify calculations of, prescribed radiation doses.
Core task · Importance 4.8/5 (O*NET) · 75% of observed AI use on this task is automation-pattern
Getting Information · 1 task
Design the arrangement of radiation fields to reduce exposure to critical patient structures, such as organs, using computers, manuals, and guides.
Core task · Importance 4.9/5 (O*NET)
Monitoring Processes, Materials, or Surroundings · 1 task
Perform quality assurance system checks, such as calibrations, on treatment planning computers.
Core task · Importance 3.9/5 (O*NET)
Evaluating Information to Determine Compliance with Standards · 1 task
Plan the use of beam modifying devices, such as compensators, shields, and wedge filters, to ensure safe and effective delivery of radiation treatment.
Core task · Importance 4.8/5 (O*NET)
Thinking Creatively · 1 task
Develop radiation treatment plans in consultation with members of the radiation oncology team.
Core task · Importance 4.7/5 (O*NET)
Handling and Moving Objects · 1 task
Fabricate beam modifying devices, such as compensators, shields, and wedge filters.
Core task · Importance 3.9/5 (O*NET)
Documenting/Recording Information · 1 task
Record patient information, such as radiation doses administered, in patient records.
Core task · Importance 4.3/5 (O*NET)
Training and Teaching Others · 1 task
Advise oncology team members on use of beam modifying or immobilization devices in radiation treatment plans.
Core task · Importance 4.2/5 (O*NET)
Task statements and work activities from the O*NET Database by the U.S. Department of Labor, Employment and Training Administration (USDOL/ETA), used under CC BY 4.0. O*NET® is a trademark of USDOL/ETA. USDOL/ETA has not reviewed or approved this material.
That's the average. Is this you?
Everything above describes Medical dosimetrists as a whole. Three questions, and nothing is stored or sent.
Where are you in your career?
What does your week mostly look like?
Is your employer deploying AI?
Where the leverage is
Upskill in AI-driven automation (e.g., Make, Zapier) and generative tools in your domain. The opportunity is to pivot from execution to directing AI-augmented workflows.
AI is transforming tasks rather than replacing entire jobs. Routine scheduling and communication automate, but human decision-making remains critical.
Master workflow automation platforms (e.g., Make, Zapier, CRM integrations) to double your output and position for fractional leadership roles.
This guidance is written per exposure band — three texts across all 342 occupations, framed on the ILO's transformation-versus-displacement distinction. It is editorial guidance for the reshaping band, not an occupation-specific research finding about medical dosimetrists.
Sources for this occupation
Only the sources that hold data for medical dosimetrists are listed. A study that does not cover this occupation is not cited here.
- Microsoft Research — Working with AI (Tomlinson et al., arXiv:2507.07935): Measured AI applicability, from 200,000 anonymised Copilot conversations classified against O*NET work activities.
- O*NET Database 31.0 (USDOL/ETA), CC BY 4.0: The occupation's task statements and work activities, verbatim. Joined on SOC 2018 with no crosswalk — O*NET-SOC is built on SOC — and carrying no claim about which tasks AI touches.
- Anthropic Economic Index — collaboration split (release 2026-06-26, CC-BY 4.0): Measured share of observed Claude usage following an automation rather than an augmentation pattern. This is the second axis of the grouping above.
- Statistics Canada NOC 2021 concordance: Official SOC 2018 → NOC 2016 V1.3 → NOC 2021 V1.0 correspondence tables. TEER is the second digit of the resolved code, so we report it only where the concordance gave a single candidate.
- ESDC COPS 2024–2033: Projected labour-market assessment per NOC 2021 unit group, from the Canadian Occupational Projection System open dataset.
- BCG — AI Will Reshape More Jobs Than It Replaces (April 3, 2026): Published aggregate reshaping rates, used to calibrate our modelled percentages. The per-occupation figures here are our estimates, not BCG's data.
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