Automation index
How much of a Podiatrist’s job can AI already do?
A file and a gait study come earlier. Cutting, operating in clinic, and not infecting do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Podiatrist
18%
AI can already do 18% of this job.
#587 / 643
View rankingPodiatrist
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 |
|---|---|---|---|
Files, gait studies and prescriptions.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 32% | +5.1 |
Design insoles and orthotic orders.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
Chiropody, minor surgery and dressing.EstimateNo public source provides a precise figure. This is our estimate. | 22% | 8% | +1.8 |
Examine, diagnose and explain.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 12% | +1.9 |
Diabetic follow-up and wounds.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 14% | +2.2 |
Clinic hygiene and care.EstimateNo public source provides a precise figure. This is our estimate. | 14% | 16% | +2.2 |
| Total | 100% | 18% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- Files, gait studies and prescriptions
- Design insoles and orthotic orders
- Chiropody, minor surgery and dressing
- Examine, diagnose and explain
- Diabetic follow-up and wounds
- Clinic hygiene and care
- podologo
- podiatrist
Sources for this job· O*NET 29-1081.00· ESCO/ISCO 2269
IS WORK OVER?
ML engineer
29%
#64 / 643
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