Automation index
How much of a Neurosurgeon’s job can AI already do?
Neuronavigation comes earlier. Going into the skull and not leaving a deficit do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Neurosurgeon
19%
AI can already do 19% of this job.
#516 / 633
View rankingNeurosurgeon
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 |
|---|---|---|---|
Indicate, consent and the plan with neurology.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 36% | +5.8 |
Craniotomy, spine and the tumour that bleeds.EstimateNo public source provides a precise figure. This is our estimate. | 24% | 6% | +1.4 |
Neuronavigation, monitoring and the margin.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 18% | +2.9 |
Neuro ICU, pupils and raised pressure.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
Family, the deficit and rehab.EstimateNo public source provides a precise figure. This is our estimate. | 14% | 28% | +3.9 |
On-call, the haematoma and signing go or no-go.EstimateNo public source provides a precise figure. This is our estimate. | 14% | 10% | +1.4 |
| Total | 100% | 19% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- craniotomy
- spine surgery
- neuronavigation
- brain tumour
- neuro ICU
- subdural haematoma
- intraoperative monitoring
- raised ICP
Sources for this job· O*NET 29-1241.00· ESCO/ISCO 2212
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