Radiation therapists

AI Overlap Index
45.8 / 100
Partially Exposed

Clear pressure on routine tasks. Composition of the role will shift within the decade.

SOC 29-1124 · Healthcare

Bureau of Labor Statistics
Median pay
$101,990/yr
Hourly
$49/hr
Jobs 2024
19,200
Projected 2034
19,600
10-yr outlook
+2% · Slower than average
Employment change
400
Entry education
Associate's degree
SOC code
29-1124

Signal composition

how the 0-100 score is assembled

Task Automation Impact weight 60%
44.6
contribution to AOI: 26.8
Automation Potential weight 10%
40.0
contribution to AOI: 4.0
Market Pressure weight 15%
55.0
contribution to AOI: 8.2
Entry Barrier Erosion weight 15%
45.0
contribution to AOI: 6.8

By seniority

multiplicative adjustment from category curve

Entry
50.4
mult 1.10x
Mid
45.8
mult 1.00x
Senior
37.6
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

Keep detailed records of treatment sessions and patient progress

AI excels at structured data entry, can auto-populate treatment records from machine logs and imaging data, generate progress summaries, and ensure documentation completeness. Voice-to-text and automated form filling can capture session details with minimal human input, requiring only brief verification—substantially reducing the labor content of this administrative task.

BLS evidence: Radiation therapists keep detailed records of treatment as part of their typical duties.

82
automation
Core t2

Determine the location of tumors to ensure correct patient positioning for treatment

AI imaging analysis systems already demonstrate high accuracy in tumor localization from CT, MRI, and PET scans, and can calculate optimal patient positioning coordinates. While a therapist must verify positioning and make final adjustments at the machine, AI can autonomously perform the bulk of the analytical work determining tumor location and generating positioning instructions.

BLS evidence: Duties include determining the location of tumors to ensure correct positioning of patients for administering each treatment.

68
automation
Important t6

Coordinate care with oncology team members including dosimetrists and oncologists

AI can facilitate care coordination by scheduling, summarizing patient status, flagging discrepancies in treatment plans, and routing communications. However, effective oncology team coordination involves nuanced clinical judgment, negotiating treatment modifications, and collaborative decision-making that benefits from human relationship dynamics and contextual understanding of each team member's expertise.

BLS evidence: Radiation therapists are part of the oncology teams that treat patients with cancer and must work well with other members of the oncology team to effectively coordinate care.

52
automation
Important t5

Monitor patients during treatment to check for unusual reactions

AI systems with camera and sensor integration can detect some physiological changes and movement during treatment, potentially flagging unusual reactions. However, monitoring requires real-time assessment of subtle patient distress signals, immediate physical intervention capability if needed, and judgment calls about whether to pause treatment—tasks where human presence remains essential for safety and patient comfort.

BLS evidence: Duties include monitoring the patient to check for unusual reactions to the treatment.

48
automation
Important t4

Explain treatment plans to patients and answer questions about treatment

AI can generate clear explanations of radiation therapy procedures and answer common questions about treatment protocols, side effects, and timelines. However, patients facing cancer treatment need empathetic human interaction, and therapists must read emotional cues, adjust explanations to individual comprehension levels, and provide psychological support that AI cannot fully replicate in high-stakes medical contexts.

BLS evidence: Radiation therapists typically explain treatment plans to the patient and answer questions about treatment.

42
automation
Core t3

Protect patients and staff from improper exposure to radiation

Radiation protection requires physical monitoring of treatment rooms, proper shielding placement, dosimeter management, and real-time intervention if safety protocols are breached. AI can monitor sensor data and flag anomalies, but the physical safety procedures, emergency response, and regulatory accountability require human execution in the clinical environment.

BLS evidence: Radiation therapists must protect the patients and themselves from improper exposure to radiation, following safety procedures.

35
automation
Core t1

Calibrate and operate linear accelerators to deliver radiation therapy to patients

Operating linear accelerators requires real-time physical manipulation of medical equipment, patient safety monitoring, and immediate response to equipment anomalies in a clinical setting. While AI can assist with calibration calculations and treatment parameters, the physical operation and safety-critical real-time adjustments require human presence and judgment that regulators and healthcare systems will not delegate to AI.

BLS evidence: Radiation therapists operate machines, such as linear accelerators, to deliver concentrated radiation therapy to the region of a patient's tumor.

25
automation
Supporting t8

Position and lift patients for radiation treatment procedures

Positioning and lifting patients requires fine motor control, physical strength, adaptation to each patient's mobility limitations and body habitus, and real-time tactile feedback in an unstructured clinical environment. Current robotics cannot safely handle the variability of patient transfers and precise positioning required for radiation therapy without extensive human supervision that negates automation benefits.

BLS evidence: Radiation therapists stand for long periods and may need to lift or turn patients.

8
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
40
karpathy 4/10
  • Karpathy/BLS Digital AI Exposure (0-10 scale rescaled to 0-100)
Market Pressure
55
outlook: Slower than average
  • BLS projected outlook: Slower than average (2%)
  • Indeed demand signal (monthly refresh pending)
Entry Barrier Erosion
45
ed: Associate's degree
  • BLS typical entry-level education: Associate's degree
  • Credential trend signal (annual refresh)

Related in Healthcare

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