Medical dosimetrists

AI Overlap Index
57.9 / 100
Mostly Exposed

Most of the workflow is automatable. Human judgment remains for exceptions, clients, or ambiguity.

SOC 29-2036 · Healthcare

Bureau of Labor Statistics
Median pay
$138,110/yr
Hourly
$66/hr
Jobs 2024
4,800
Projected 2034
4,900
10-yr outlook
+3% · As fast as average
Employment change
200
Entry education
Bachelor's degree
SOC code
29-2036

Signal composition

how the 0-100 score is assembled

Task Automation Impact weight 60%
63.1
contribution to AOI: 37.9
Automation Potential weight 10%
80.0
contribution to AOI: 8.0
Market Pressure weight 15%
45.0
contribution to AOI: 6.8
Entry Barrier Erosion weight 15%
35.0
contribution to AOI: 5.2

By seniority

multiplicative adjustment from category curve

Entry
63.7
mult 1.10x
Mid
57.9
mult 1.00x
Senior
47.5
mult 0.82x

Entry-level roles carry the brunt because they concentrate the most automatable subset of tasks. Senior work is insulated by judgment, relationships, and accountability.

Task-level analysis

scored 0-100 for current-generation AI feasibility, weighted by BLS-stated importance

8 tasks · model: claude-sonnet-4-5-20250929
Supporting t7

Document treatment provided to patients

Clinical documentation of standardized treatment parameters, doses delivered, and equipment settings is highly structured and rule-based. AI can auto-populate treatment records from system logs and structured inputs with minimal human review, similar to medical transcription tasks.

BLS evidence: Medical dosimetrists document treatment provided to a patient and keep records of each patient's treatment.

88
automation
Important t3

Review patient medical imaging scans and documents

AI excels at medical image analysis, with systems capable of identifying anatomical structures, tumor boundaries, and organs at risk from CT, MRI, and PET scans. Document review for relevant clinical information is also highly automatable via NLP, though critical findings still warrant human verification.

BLS evidence: Medical dosimetrists review patient records, such as computer tomography (CT) and magnetic resonance imaging (MRI) scans.

75
automation
Core t1

Calculate the proper dose of radiation to be administered to patients

AI systems can now perform complex radiation dose calculations with high accuracy, incorporating patient anatomy, tumor characteristics, and physics constraints. Modern treatment planning systems already heavily automate this, though human dosimetrists verify outputs and handle edge cases requiring clinical judgment.

BLS evidence: Medical dosimetrists calculate the exact dose and angle of radiation to be administered, making these calculations both manually and with computers.

72
automation
Supporting t8

Monitor radiation exposure levels using specialized devices

Radiation monitoring involves reading dosimeter devices and comparing values against safety thresholds—tasks well-suited to sensor integration and automated analysis. AI can continuously monitor, log, and alert on exposure levels, though physical badge collection and occasional equipment troubleshooting require human involvement.

BLS evidence: Medical dosimetrists use radiation monitoring devices to measure radioactivity levels in patients.

70
automation
Core t2

Design radiation-delivery treatment plans for cancer patients

AI-driven treatment planning systems can generate optimized radiation delivery plans by analyzing 3D imaging, tumor volumes, and dose constraints. Deep learning models have demonstrated ability to create clinically acceptable plans, though human review remains standard for plan selection and refinement based on nuanced clinical factors.

BLS evidence: Medical dosimetrists consult with other members of the radiation oncology team and design the radiation-delivery plan for patients.

68
automation
Important t5

Perform quality assurance checks of radiation treatment equipment

Quality assurance protocols for radiation equipment involve standardized measurement sequences and comparison against tolerance thresholds that AI can execute and flag deviations. However, physical setup, equipment calibration, and interpretation of anomalous results still require human expertise and hands-on intervention.

BLS evidence: Medical dosimetrists perform quality assurance checks of treatment equipment and calibrate equipment to ensure accuracy.

62
automation
Important t4

Consult with radiation oncology team members on treatment planning

Consultation requires real-time collaborative discussion, negotiation of treatment tradeoffs, and integration of multidisciplinary perspectives that AI cannot fully replicate. While AI can prepare data and suggest options, the interpersonal dynamics and shared decision-making are fundamentally human activities.

BLS evidence: Medical dosimetrists consult with other members of the radiation oncology team and design the radiation-delivery plan for patients.

35
automation
Supporting t6

Assist in designing immobilization devices to position patients during treatment

Designing immobilization devices requires physical assessment of patient anatomy, trial-and-error fitting, and custom fabrication in a hands-on environment. While AI could suggest designs from imaging, the physical prototyping and patient-specific adjustments demand manual dexterity and in-person problem-solving.

BLS evidence: Medical dosimetrists may assist in designing molds, casts, and other immobilization devices to position patients during treatment.

25
automation

Task heatmap

automation score by task, sorted by weighted contribution

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External signals and sources

category-level priors and BLS fields that feed the four non-task signals

Automation Potential
80
karpathy 8/10
  • Karpathy/BLS Digital AI Exposure (0-10 scale rescaled to 0-100)
Market Pressure
45
outlook: As fast as average
  • BLS projected outlook: As fast as average (3%)
  • Indeed demand signal (monthly refresh pending)
Entry Barrier Erosion
35
ed: Bachelor's degree
  • BLS typical entry-level education: Bachelor's degree
  • Credential trend signal (annual refresh)

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