Automation index
How much of a Radiation therapist’s job can AI already do?
Guided set-up comes earlier. Immobilising every day and not missing the field do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Radiation therapist
24%
AI can already do 24% of this job.
#283 / 633
View rankingRadiation therapist
Where the time goes
Task weights are our estimates based on O*NET frequency data, not time shares published by O*NET.
The time saving for each task is estimated separately.
| Task | Share | AI | Points |
|---|---|---|---|
Plan, IGRT and the day’s set-up.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 40% | +6.4 |
Immobilise, tattoo and treat fraction by fraction.EstimateNo public source provides a precise figure. This is our estimate. | 22% | 12% | +2.6 |
Check images, shifts and call the physician.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
Side effects, the patient who cries and the wait.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
Machine QA and alarms.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 32% | +5.1 |
Today’s dose and not irradiating the wrong thing.EstimateNo public source provides a precise figure. This is our estimate. | 14% | 14% | +2 |
| Total | 100% | 24% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- set up for RT
- IGRT
- immobilise patients
- deliver fractions
- linac QA
- reference tattoos
- shifts
- radiotherapy
Sources for this job· O*NET 29-1124.00· ESCO/ISCO 3211
IS WORK OVER?
ML engineer
29%
#64 / 633
View rankingML engineer
Where the time goes
Task weights are our estimates based on O*NET frequency data, not time shares published by O*NET.
The time saving for each task is estimated separately.
| Task | Share | AI | Points |
|---|---|---|---|
Train models, features and notebooks.EstimateNo public source provides a precise figure. This is our estimate. | 22% | 48% | +10.6 |
Evaluate, document and compare runs.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 42% | +7.6 |
Deploy, monitor and retrain.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
Talk to product about what a good error is.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 15% | +2.4 |
Dirty data, bias and model limits.EstimateNo public source provides a precise figure. This is our estimate. | 14% | 18% | +2.5 |
On-call, incidents and rollback.EstimateNo public source provides a precise figure. This is our estimate. | 14% | 16% | +2.2 |
| Total | 100% | 29% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- Train models, features and notebooks
- Evaluate, document and compare runs
- Deploy, monitor and retrain
- Talk to product about what a good error is
- Dirty data, bias and model limits
- On-call, incidents and rollback
- machine learning engineer
- ingeniero ml
- mlops
Sources for this job· O*NET 15-1221.00· ESCO/ISCO 2511