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?

23%

AI can already do 23% of this job.

#293 / 633

Dermatologist

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. Estimate No public source provides a precise figure. This is our estimate.	16%	40%	+6.4
Clinic, dermoscopy and biopsy. Estimate No public source provides a precise figure. This is our estimate.	20%	12%	+2.4
Explain, treat and follow doubtful lesions. Estimate No public source provides a precise figure. This is our estimate.	16%	22%	+3.5
Reports, prescriptions and pathology notes. Estimate No public source provides a precise figure. This is our estimate.	16%	32%	+5.1
Minor surgery, cryotherapy and dressings. Estimate No public source provides a precise figure. This is our estimate.	16%	18%	+2.9
Diary, team and not missing a melanoma. Estimate No 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

IS WORK OVER?

31%

#33 / 633

Information specialist

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
Classify, index and describe holdings. Estimate No public source provides a precise figure. This is our estimate.	18%	48%	+8.6
Thesauri, metadata and the internal search. Estimate No public source provides a precise figure. This is our estimate.	16%	52%	+8.3
Answer queries and retrieve what nobody can find. Estimate No public source provides a precise figure. This is our estimate.	16%	28%	+4.5
Records policy, retention and destruction. Estimate No public source provides a precise figure. This is our estimate.	18%	22%	+4
Train people who file badly and people who do not file. Estimate No public source provides a precise figure. This is our estimate.	16%	18%	+2.9
The organisation’s memory, and not losing it. Estimate No public source provides a precise figure. This is our estimate.	16%	14%	+2.2
Total	100%		30%

Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low