29-2034.00

Radiologic and MRI technologists

272,000 workers$78,980/yr medianAssociate's degree
2.6

Lower displacement risk

Composite of 5 dimensions (higher = more displacement pressure)

AI Exposure Analysis

GPT-scored exposure4/10

While AI is revolutionizing image analysis and quality control, the core of this role requires physical presence to position patients, administer contrast agents, and operate heavy machinery in a clinical setting. AI will significantly enhance productivity and image evaluation, but the physical and interpersonal requirements of patient care provide a substantial barrier to full automation.

Dimension Breakdown

Technical AI Exposurepressure
4.0

How many of this occupation's tasks can current or near-term AI systems perform? Based on GPT-scored analysis of 342 BLS occupations validated against 6 academic exposure indices.

Institutional Adoption Speedpressure
0.0

How quickly will firms in this sector actually deploy AI? Accounts for regulatory burden, digital maturity, competitive pressure, union density, and organizational complexity.

Worker Adaptabilityabsorption
5.5

How well can workers in this group transition to new roles? Composite of net liquid wealth (financial buffer), skill transferability, geographic job density, and age demographics.

Demand Elasticityabsorption
5.0

When AI makes this sector's output cheaper, does demand expand enough to offset job losses? High elasticity means the Jevons Paradox may preserve or even grow employment.

AI Complementarityabsorption
9.9

Is AI primarily enhancing workers in this occupation or replacing them? Based on CFO survey data where available, estimated from task composition and job dimensionality otherwise. Jobs with more distinct task clusters (high dimensionality) tend toward augmentation via the O-Ring "focus effect" — automating some tasks lets workers concentrate on remaining ones, multiplying output quality.

5 task dimensionsHigh-dimensional

Task heuristic base: 8.9 +1.0 from dimensionality

Task Composition

How this occupation's work time is distributed across 8 task categories, based on O*NET work activity data.

Information Processing21%
Interpersonal19%
Coordination & Mgmt17%
Technical / Specialized13%
Analysis & Decision12%
Communication9%
Physical / Manual8%
Creative / Generative2%

Top Work Activities

Most important work activities from O*NET, ranked by importance score (1-5).

ActivityCategoryScore
Assisting and Caring for OthersInterpersonal4.87
Working with ComputersInformation Processing4.29
Updating and Using Relevant KnowledgeInformation Processing4.04
Documenting/Recording InformationTechnical / Specialized4.04
Controlling Machines and ProcessesTechnical / Specialized4.02
Handling and Moving ObjectsPhysical / Manual3.95
Performing for or Working Directly with the PublicInterpersonal3.95
Getting InformationInformation Processing3.90
Establishing and Maintaining Interpersonal RelationshipsInterpersonal3.85
Monitoring Processes, Materials, or SurroundingsInformation Processing3.82

Methodology

This page combines Karpathy's GPT-scored technical exposure (per-occupation) with four additional dimensions inherited from the parent SOC major group: institutional adoption speed, worker adaptability, demand elasticity, and AI complementarity.

Task composition is derived from O*NET work activity data, mapped to 8 internal categories. The complementarity score is adjusted by job dimensionality (Gans & Goldfarb 2024): occupations with more distinct task clusters tend toward augmentation rather than replacement.

Net displacement risk is computed as a weighted composite: exposure (30%), adoption speed (20%), adaptability (15%), demand elasticity (15%), complementarity (20%). Pressure dimensions are normalized independently from absorption dimensions, so defensive factors can fully counterbalance exposure.