Automation index
How much of a Cardiac surgeon’s job can AI already do?
A bypass sketch comes earlier. Opening the chest and coming off pump do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Cardiac surgeon
18%
AI can already do 18% of this job.
#570 / 643
View rankingCardiac surgeon
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 |
|---|---|---|---|
Indication, consent and the plan with cardiology.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 32% | +5.1 |
Bypass, valve and pump time.EstimateNo public source provides a precise figure. This is our estimate. | 24% | 6% | +1.4 |
Cannulate, protect the muscle and come off bypass.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 18% | +2.9 |
Cardiac ICU, bleeding and tamponade.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
Follow-up, the wound and the family at the door.EstimateNo public source provides a precise figure. This is our estimate. | 14% | 28% | +3.9 |
On-call, the infarct that does not wait, and the sign-off.EstimateNo public source provides a precise figure. This is our estimate. | 14% | 10% | +1.4 |
| Total | 100% | 18% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- coronary bypass
- valve surgery
- cardiopulmonary bypass
- open the chest
- cardiac ICU
- tamponade
- cannulate
- myocardial protection
Sources for this job· O*NET 29-1241.00· ESCO/ISCO 2212
IS WORK OVER?
ML engineer
29%
#64 / 643
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