Automation index
How much of a Haematologist’s job can AI already do?
A blood count and a first outline come earlier. Calling a transplant and talking to the family do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Haematologist
23%
AI can already do 23% of this job.
#297 / 633
View rankingHaematologist
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 |
|---|---|---|---|
Read blood counts, smears and marrow studies.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 48% | +7.7 |
Clinic, examination and the plan with the patient.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 18% | +3.2 |
Order chemo, anticoagulants and transfusion support.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
Procedures: marrow, apheresis and venous access.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 12% | +2.2 |
Tumour board, consults and the ward day.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
On-call, haematology emergency and signing the treatment.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 14% | +2.2 |
| Total | 100% | 23% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- read blood counts
- order transfusions
- treat anaemias
- diagnose leukaemias
- apheresis
- haematology clinic
- marrow studies
- anticoagulation
Sources for this job· O*NET 29-1229.03· ESCO/ISCO 2212
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