Automation index
How much of a Orthotic and prosthetic technician’s job can AI already do?
A scan and a CAD tweak come earlier. A prosthesis that takes the step and does not rub do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Orthotic and prosthetic technician
22%
AI can already do 22% of this job.
#396 / 633
View rankingOrthotic and prosthetic technician
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 |
|---|---|---|---|
Measure, scan and design the orthosis.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 40% | +6.4 |
Thermoform, laminate and assemble parts.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 14% | +2.5 |
Trial, align and the one who will not take the step.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 18% | +3.2 |
Repair, grow-with-the-child and reviews.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
Prescription, approval and working with rehab.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
No rubbing and owning the support.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 12% | +1.9 |
| Total | 100% | 22% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- make prostheses
- orthoses
- scan residual limbs
- align prostheses
- thermoform
- laminate carbon
- review fittings
- O&P
Sources for this job· O*NET 51-9082.00· ESCO/ISCO 3214
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