Automation index
How much of a Dental hygienist’s job can AI already do?
An appointment and a reminder come earlier. Scaling, probing and not hurting the gum do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Dental hygienist
20%
AI can already do 20% of this job.
#454 / 633
View rankingDental hygienist
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 |
|---|---|---|---|
Diary, charts and recall reminders.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 32% | +5.1 |
Scaling, polish and periodontal probing.EstimateNo public source provides a precise figure. This is our estimate. | 22% | 10% | +2.2 |
Teach brushing, diet and maintenance.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
Support the dentist in clinic and X-ray.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 18% | +2.9 |
Clinical notes and progress photos.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
Sterilisation, bay and not infecting anyone.EstimateNo public source provides a precise figure. This is our estimate. | 14% | 14% | +2 |
| Total | 100% | 20% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- Diary, charts and recall reminders
- Scaling, polish and periodontal probing
- Teach brushing, diet and maintenance
- Support the dentist in clinic and X-ray
- Clinical notes and progress photos
- higienista dental
- dental hygienist
Sources for this job· O*NET 29-1292.00· ESCO/ISCO 2266
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