Automation index
How much of a Occupational physician’s job can AI already do?
A screening protocol comes earlier. Calling fit or not and facing the company do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Occupational physician
29%
AI can already do 29% of this job.
#65 / 623
View rankingOccupational 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 |
|---|---|---|---|
Screenings, protocols and the work history.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 52% | +9.4 |
Fit notes, restrictions and PPE.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 40% | +6.4 |
The hard case, the one who cannot, and the company pushing.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 18% | +3.2 |
Risk surveillance, sick leave and return.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
Inspectorate, mutual and coordination with H&S.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
Medical secrecy and signing fit for work.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 14% | +2.2 |
| Total | 100% | 29% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- occupational screening
- fit for work
- health surveillance
- work restrictions
- return to work
- occupational medicine
- work history
- medical secrecy
Sources for this job· O*NET 29-1229.00· 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