Automation index
How much of a Nuclear medicine physician’s job can AI already do?
A PET and a stock report come earlier. Dosing the isotope and reading the patient do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Nuclear medicine physician
27%
AI can already do 27% of this job.
#168 / 623
View rankingNuclear medicine physician
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 |
|---|---|---|---|
PET, SPECT protocols and a stock report.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 52% | +9.4 |
Order dose, timing and preparation.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
Give the isotope and watch the reaction.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 14% | +2.5 |
Clinic, the cancer that returns and the family.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
Quality, waste and radiation protection.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 32% | +5.1 |
Signing the study and the dose you cannot repeat.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 12% | +1.9 |
| Total | 100% | 27% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- report PET
- SPECT
- dose isotopes
- nuclear medicine
- radiation protection
- PET protocols
- give radiopharmaceuticals
- nuclear oncology clinic
Sources for this job· O*NET 29-1224.00· ESCO/ISCO 2212
IS WORK OVER?
ML engineer
29%
#64 / 623
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