Automation index
How much of a Internist’s job can AI already do?
A differential and a first plan come earlier. Seeing the whole complex patient does not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Internist
24%
AI can already do 24% of this job.
#273 / 633
View rankingInternist
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 |
|---|---|---|---|
Cross labs, imaging and a differential.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 48% | +8.6 |
Ward round, examination and decisions.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 14% | +2.5 |
Explain to family and ceilings of care.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
Discharge reports, consults and boards.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
Medical on-call and difficult patients.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 18% | +2.9 |
Team, residents and not fragmenting the patient.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 14% | +2.2 |
| Total | 100% | 24% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- care for patients
- diagnose
- nursing care
- clinical notes
- Cross labs, imaging and a differential
- Ward round, examination and decisions
- Explain to family and ceilings of care
- Discharge reports, consults and boards
- Medical on-call and difficult patients
- internista
Sources for this job· O*NET 29-1216.00· 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