Automation index
How much of a Infectious disease physician’s job can AI already do?
An antibiogram comes earlier. Picking the antibiotic and not breeding resistance do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Infectious disease physician
23%
AI can already do 23% of this job.
#298 / 633
View rankingInfectious disease 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 |
|---|---|---|---|
Read cultures, PCR and the antibiogram.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 48% | +7.7 |
Consult, the febrile patient and the one who does not respond.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 18% | +3.2 |
Antibiotic plan, HIV and tropical disease.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
Stewardship, resistance and the hospital protocol.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
Isolation, public health and the ward outbreak.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 14% | +2.5 |
On-call and signing the last-resort drug.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
- antibiogram
- prescribe antibiotics
- stewardship
- treat HIV
- ID consult
- isolation
- pathogen PCR
- resistance
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