Automation index
How much of a Dermatologist’s job can AI already do?
A photo of a lesion and a first differential come earlier. Biopsy, looking and saying it to the face do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Dermatologist
23%
AI can already do 23% of this job.
#293 / 633
View rankingDermatologist
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 |
|---|---|---|---|
Review photos, history and a first differential.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 40% | +6.4 |
Clinic, dermoscopy and biopsy.EstimateNo public source provides a precise figure. This is our estimate. | 20% | 12% | +2.4 |
Explain, treat and follow doubtful lesions.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
Reports, prescriptions and pathology notes.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 32% | +5.1 |
Minor surgery, cryotherapy and dressings.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 18% | +2.9 |
Diary, team and not missing a melanoma.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 14% | +2.2 |
| Total | 100% | 22% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- Clinic, dermoscopy and biopsy
- Explain, treat and follow doubtful lesions
- Reports, prescriptions and pathology notes
- Minor surgery, cryotherapy and dressings
- Diary, team and not missing a melanoma
- dermatologo
Sources for this job· O*NET 29-1213.00· 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