Automation index
How much of a Chiropractor’s job can AI already do?
A film and a report come earlier. The adjustment, and not hurting the neck, do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Chiropractor
20%
AI can already do 20% of this job.
#466 / 633
View rankingChiropractor
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 |
|---|---|---|---|
History, films and a first plan.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 32% | +5.1 |
Adjustment, mobilisation and spinal work.EstimateNo public source provides a precise figure. This is our estimate. | 22% | 10% | +2.2 |
Explain, dose and watch red flags.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
Notes, consents and progress.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
Room, hygiene and follow-up.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 16% | +2.6 |
Not over-adjusting or treating the wrong person.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
- History, films and a first plan
- Adjustment, mobilisation and spinal work
- Explain, dose and watch red flags
- Notes, consents and progress
- Room, hygiene and follow-up
- quiropractico
- chiropractor
Sources for this job· O*NET 29-1011.00· ESCO/ISCO 2269
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