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Dermatologist
A medical doctor for the skin, hair and nails. Dermatologists examine the whole skin and single lesions, often through a dermatoscope, decide what to biopsy or cut out, operate on skin cancers, manage long-running conditions such as psoriasis, eczema and acne, and do cosmetic and laser procedures. So much of the work is visual that the first look at a lesion is the part machines reach first. Image classifiers trained on skin photographs can sort referrals and flag suspicious moles, and language tools draft notes and letters. The hands, the history and the signature stay with the dermatologist: biopsy and surgery, treatment adjusted over months, and answering for a missed melanoma.
Go through last month's photo referrals you triaged and mark the ones you discharged from the photograph alone. That is the share a classifier would compete for, and you can show it to your clinical lead.
This is not a probability of losing your job. It combines how much of the role's task load is exposed to automation with how far adoption has actually gone — useful for comparing occupations on one consistent basis, and for nothing else.
Written for dermatologists, including dermatologic and Mohs surgeons, in public health systems and private practice. General practitioners, pathologists (who read skin biopsies), surgeons and nurse practitioners have their own pages. Aestheticians who give cosmetic treatments without a medical licence do a different job. Judgements marked as platform inference reason from what each task is made of and from what current technology does in general; they are not measurements of this occupation.
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What is actually changing#
The unit of analysis is the task, not the job title. A role is not replaced — its task mix shifts.
Each tile is one task. Its size is how much of the job it is; its colour is where the task is heading. Click a tile to see what the judgement does not establish.
This says who owns the decision. It does not say how much of the dermatologist's time the task will take. Software that maps and pre-sorts lesions can shorten each examination and let fewer dermatologists see more patients. A dated deployment in which a classifier decides, with no dermatologist's review, which lesions in a specialist clinic are left alone would move this towards automating.
This says that the clearly benign end of triage can run without a specialist. It does not say how much of the work sits at that end. A missed cancer in an automated pathway could also bring back a rule requiring a human reader. The judgement would change with dated evidence that health systems withdrew autonomous triage after safety failures, or that its use stayed at pilot scale.
Keeping the work in human hands does not keep the volume. If triage and screening software sends fewer benign lesions for removal, the number of procedures could fall even though a person still does every one. Under local rules, part of this work can also pass to nurses and physician associates. A dated deployment of a device that performs skin excisions under supervision alone would move this judgement.
This rests on how hard rashes are for current systems and on prescribing rules, and either can change. A general practitioner with decision support may also keep cases that used to be referred, which would shrink the dermatologist's share without any machine treating a patient. The judgement would move with evidence that a decision-support or image system diagnoses common rashes accurately across skin types and is used to close cases without referral.
The pressure on this work comes less from automation than from delegation. In many places nurses and aesthetic practitioners already do these procedures, and the rules on who may do them vary and change. This judgement does not cover that competition. A dated deployment of devices that deliver treatment with no operator beyond setup, in routine use across clinics, would move it.
Time saved on paperwork can go into seeing more patients instead of doing less work, so this does not say whether fewer dermatologists will be needed. The judgement would change with evidence that drafted notes are routinely filed without clinician review, or that dermatology clinics use AI drafting at scale with measured effects on time or errors.
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Recent changes#
Chelsea and Westminster Hospital NHS Foundation Trust's own account (published 2024-12-09) of its teledermatology pathway for urgent suspected skin cancer referrals: since a 2022 pilot it has assessed over 10,000 patients, and from December 2024 the service extended Trust-wide to West Middlesex University Hospital. The system, Skin Analytics' DERM, reads dermatoscope images taken with a smartphone. The Trust states autonomous AI identifies and discharges patients with benign lesions without a dermatologist's review, and reports 97% sensitivity for skin cancer and over 35% of dermatologist appointments released, as its own figures rather than an independent audit. This covers one NHS Trust's urgent suspected skin cancer pathway in England; it says nothing about routine, non-urgent dermatology referrals, private practice, or other health systems, and the accuracy figures are self-reported by the Trust and the AI vendor, Skin Analytics.
An employer has put it into production. Can move the baseline — weighted by scale and how similar the setting is.
What this means for you#
If you are training, expect software to take the photograph sorting and simple mole checks first, and that work has long filled junior clinics, so your learning cases may come from elsewhere. Build what it does not do: examining the whole patient, rashes on every skin type, biopsy and surgery, and treatment managed over time.
Expect referrals to arrive already sorted by software, and more of your clinic to be the hard cases it passes on. Your signature, your surgery and your judgement on complex disease stay with you. The time you spend on routine reassurance is what shrinks.
Your options#
Four directions, each with its real constraints and one thing you can test this week. Continuing as you are is a legitimate choice — it just has to be a chosen one.
Run the triage, not just read it
Photograph triage is the part most likely to run on software. Someone clinical still has to set its thresholds, audit what it misses and handle the cases it passes up.
At first this is often unpaid time on top of clinics. It needs audit and clinical governance skills, and it only exists where your employer adopts such tools.
Go through last month's photo referrals you triaged and mark the ones you discharged from the photograph alone. That is the share a classifier would compete for, and you can show it to your clinical lead.
Lean into skin surgery
Biopsy, excision and Mohs surgery are hands-on, decided during the cut and carry personal liability. Of all the work in the job, they are the furthest from what software does.
A surgical fellowship means extra training time, competitive places and lower income while you train. Some procedures may still be delegated to other staff.
List last month's procedures by type and mark the ones you could not hand to a nurse. Then email one fellowship director to ask what their programme requires.
Evaluate skin-imaging software
Health systems, device makers and regulators need dermatologists to label images, design studies and judge whether a classifier is safe across skin types.
There are fewer of these roles than clinical posts. They may mean giving up some clinical sessions, and industry work brings conflict-of-interest declarations.
Find out whether your hospital or professional society has a group reviewing AI skin tools, and ask to sit in on its next meeting.
Common questions#
Not as a whole job. Software is strong at the part that is a picture: grading photographs of moles and sorting referrals. That step can run without a specialist for clearly harmless lesions. Examination, biopsy, skin surgery, treatment of chronic disease and responsibility for a diagnosis stay with the dermatologist.
We do not answer that with a number of years. Watch two things where you work. First, whether skin referrals are closed by software without a dermatologist seeing them. Second, whether the rules that make a doctor answerable for a diagnosis begin to accept a device in the doctor's place. Those tell you more than any date.
On clean photographs of moles, image classifiers can match specialists at telling melanoma from a harmless mole, but that is a narrow test. In a clinic the lesion has to be found first, photographed well and weighed against the patient's history. Classifiers are also weaker on rashes, unusual sites and skin types that are scarce in their training data. The useful question is not who wins a picture test but who answers when a cancer is missed.
The parts of the job that need hands and carry accountability are the ones that hold: surgery, biopsy and complex medical dermatology. The parts most likely to thin out are photograph triage and routine mole reassurance, which is often where trainees learn. Choose a training post that gives you procedures and complex clinics, not only screening lists.
For reassurance about one mole, a phone app may be some people's first stop, and that can take simple visits away from clinics. An app cannot examine the whole skin, take a biopsy or treat anything, and a worrying result still sends you to a doctor.
What these judgements rest on#
1 of 6 task judgements on this page are backed by a verified event and 5 are platform inference, each labelled where it appears. Behind them sit 3 technology dimensions, a reconstructed trajectory since language models reached the public, and 1 verified events.
See which technologies, how it got here, and the method →
Where it sits in the official classification: skills, knowledge, related jobs →
Other roles in the same function#
A company divides its work into functions before it divides it into jobs. These sit in Core delivery (industry-specific) alongside this one — a fact about org charts, not a judgement that they are similar or that they are changing in the same direction.
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