Automation index
How much of a Rheumatologist’s job can AI already do?
A serology and a DAS28 come earlier. Starting the biologic and not leaving the patient stiff do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Rheumatologist
23%
AI can already do 23% of this job.
#312 / 633
View rankingRheumatologist
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 serologies, rheumatoid factor and imaging.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 48% | +7.7 |
Examine joints and call the flare.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 18% | +3.2 |
Biologics, steroids and methotrexate.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
Inject, educate and watch side effects.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
A long clinic, pain you cannot see, and the wait.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 14% | +2.5 |
Board, pharmacy and owning the immunosuppressant.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
- treat arthritis
- prescribe biologics
- examine joints
- inject joints
- read serologies
- DAS28
- rheumatology clinic
- methotrexate
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