Automation index
How much of a Allergist’s job can AI already do?
An IgE panel comes earlier. Doing the challenge and not triggering anaphylaxis do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Allergist
23%
AI can already do 23% of this job.
#286 / 633
View rankingAllergist
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 |
|---|---|---|---|
Read IgE, prick tests and the reaction history.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 48% | +7.7 |
Skin tests, challenges and immunotherapy.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 14% | +2.5 |
Avoidance, adrenaline and allergic asthma.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
Clinic for children, mites and the one who cannot eat.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 18% | +3.2 |
Letters for school, canteen and the plane.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
Anaphylaxis, on-call and owning the vaccine.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 12% | +1.9 |
| Total | 100% | 23% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- allergy tests
- prick test
- allergy vaccines
- read IgE
- treat anaphylaxis
- allergy clinic
- supervised challenge
- adrenaline
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