Automation index
How much of a Intensivist’s job can AI already do?
A ventilation protocol comes earlier. Intubating on time and talking to the family at 3 a.m. do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Intensivist
20%
AI can already do 20% of this job.
#455 / 633
View rankingIntensivist
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 |
|---|---|---|---|
Protocols, scores and the on-call plan.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 36% | +5.8 |
Airway, vasopressors and the patient who crashes.EstimateNo public source provides a precise figure. This is our estimate. | 22% | 8% | +1.8 |
Ventilation, balances and taking the tube out.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
Bedside ultrasound and ICU procedures.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 18% | +2.9 |
Family, limits and the yes or no to admission.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
On-call, the error and owning the bed.EstimateNo public source provides a precise figure. This is our estimate. | 14% | 10% | +1.4 |
| Total | 100% | 20% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- intubate
- ICU ventilation
- vasopressors
- difficult airway
- bedside ultrasound
- limit treatment
- ICU scores
- extubate
Sources for this job· O*NET 29-1229.00· 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