Automation index
How much of a Biomedical engineer’s job can AI already do?
A protocol and a model come earlier. Kit that does not fail in theatre does not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Biomedical engineer
25%
AI can already do 25% of this job.
#230 / 623
View rankingBiomedical 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 |
|---|---|---|---|
Design, validate and document clinical devices and software.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 48% | +8.6 |
Tests, MDR and risk.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 32% | +5.1 |
Hospital, clinicians and maintenance.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
Vendors, quality and traceability.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 18% | +2.9 |
User training and post-market watch.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 16% | +2.9 |
Not putting a half-built prototype in theatre.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 14% | +2.2 |
| Total | 100% | 25% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- care for patients
- diagnose
- nursing care
- clinical notes
- Tests, MDR and risk
- Hospital, clinicians and maintenance
- Vendors, quality and traceability
- User training and post-market watch
- ingeniero biomedico
- biomedical engineer
Sources for this job· O*NET 17-2031.00· ESCO/ISCO 2149
IS WORK OVER?
ML engineer
29%
#64 / 623
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