Share and intensity of work current AI systems can materially affect.
SOC 29-1213
Dermatologists AI displacement risk
AI skin-image classifiers match specialists on narrow lesion benchmarks — the famous case study. Full-body exams, biopsy and surgery decisions, cosmetic procedures, and atypical presentations keep dermatologists diagnostically accountable.
Likely potential for exposed tasks to move to software after workflow integration.
Benchmark performance is not workflow replacement: screening tools triage, they do not biopsy. Dermatology combines visual diagnosis with high procedural volume, and teledermatology triage expands referral flow rather than shrinking the specialty.
Distribution
Where Dermatologists sits across 620 tracked roles
Displacement pressure 24 — higher than 32% of the 620 occupations tracked on displacement.ai.
Score version
This page uses Seed model v0.4 (seed-v0.4-2026-05), last reviewed 2026-08-08. Directional occupation-level planning model using hand-reviewed public research, task exposure estimates, wage context, and transition-pathway assumptions.
18 O*NET task statements matched to SOC 29-1213. The displayed task profile combines these official task statements with the current public score model.
Median wage context: $328,730 (May 2025, US national). The latest BLS row matched SOC 29-1213.
Scores are planning signals, not forecasts. Local hiring demand, employer-specific workflows, licensing, and credentials must be validated before making career decisions.
2030 economic stress test
How Anthropic's scenarios classify Dermatologists
SOC 29-1213 places this role in the paper's cognitive occupation group. These group-level outcomes do not change the 24/100 role score and are not an occupation forecast.
+0.4% group wage
-0.5% cognitive employment since mid-2026; 2.9% cognitive unemployment.
Economy-wide: +1.6% GDP and 3.9% unemployment.
-0.3% group wage
-3.9% cognitive employment since mid-2026; 4.5% cognitive unemployment.
Economy-wide: +8.3% GDP and 4.6% unemployment.
-11.5% group wage
-21.5% cognitive employment since mid-2026; 17.9% cognitive unemployment.
Economy-wide: +32.4% GDP and 11.9% unemployment.
Compare the assumptions and limitations across all three scenarios. Source: The Anthropic Institute Working Paper No. 2026-02.
Official task evidence
O*NET task matches for Dermatologists
The current evidence import matched 18 task statements from Task Statements 31.0 (August 2026). These rows are used as a grounding layer for judging which parts of the occupation are repeatable, language-heavy, analytical, social, physical, or compliance-sensitive.
Dataset31.0 (August 2026)
Matched tasks18
SOC29-1213
- Core task / ID 17094
Conduct complete skin examinations.
- Core task / ID 17092
Diagnose and treat pigmented lesions such as common acquired nevi, congenital nevi, dysplastic nevi, Spitz nevi, blue nevi, or melanoma.
- Core task / ID 17091
Perform incisional biopsies to diagnose melanoma.
- Core task / ID 17090
Perform skin surgery to improve appearance, make early diagnoses, or control diseases such as skin cancer.
- Core task / ID 17093
Counsel patients on topics such as the need for annual dermatologic screenings, sun protection, skin cancer awareness, or skin and lymph node self-examinations.
- Core task / ID 17095
Diagnose and treat skin conditions such as acne, dandruff, athlete's foot, moles, psoriasis, or skin cancer.
Source: O*NET Resource Center, Task Statements. Raw import target: data/raw/onet/task-statements-31-0.txt.
Task profile
Where AI changes the work
Examine skin and diagnose conditions
Exposure 44, automation 19%, augmentation 68%.
O*NET evidence: Diagnose and treat skin conditions such as acne, dandruff, athlete's foot, moles, psori... (ID 17095)
Perform biopsies and skin surgery
Exposure 14, automation 4%, augmentation 28%.
O*NET evidence: Perform skin surgery to improve appearance, make early diagnoses, or control diseases s... (ID 17090)
Prescribe and administer treatments
Exposure 26, automation 9%, augmentation 46%.
O*NET evidence: Prescribe hormonal agents or topical treatments such as contraceptives, spironolactone,... (ID 17089)
Counsel patients on screening and prevention
Exposure 24, automation 7%, augmentation 44%.
O*NET evidence: Counsel patients on topics such as the need for annual dermatologic screenings, sun pro... (ID 17093)
TaskExposureAutomationAugmentation
Examine skin and diagnose conditions4419%68%
Perform biopsies and skin surgery144%28%
Prescribe and administer treatments269%46%
Counsel patients on screening and prevention247%44%
Transition pathways
Adjacent moves that preserve existing skills
credentialed transition
LowMohs Surgeon
Training horizon: 12-24 months. Skill overlap 70. Wage preservation signal 126.
- Complete a surgical fellowship
- Build skin cancer surgery volume
- Master reconstruction techniques
role redesign
LowTeledermatology Program Lead
Training horizon: 3-6 months. Skill overlap 66. Wage preservation signal 100.
- Design triage workflows
- Audit AI screening accuracy
- Measure referral outcomes
Comparison guides
Compare the next move before you commit
12-24 months
Dermatologists to Mohs Surgeon
Compare AI displacement pressure, wage preservation, skill overlap, training time, and first proof project for moving from Dermatologists into Mohs Surgeon.
3-6 months
Dermatologists to Teledermatology Program Lead
Compare AI displacement pressure, wage preservation, skill overlap, training time, and first proof project for moving from Dermatologists into Teledermatology Program Lead.
What the AI risk score means for Dermatologists
The displacement pressure score for Dermatologists is 24. That score blends task exposure, automation pressure, augmentation potential, wage vulnerability, transition feasibility, and source confidence. It is designed to help workers and workforce teams decide where to act first, not to claim a specific date when a job will disappear.
For this role, the clearest risk pattern is visible at the task level. Examine skin and diagnose conditions carries 19% automation pressure, while Examine skin and diagnose conditions carries 68% augmentation potential. That means the best response is usually a targeted redesign of work: move away from repeatable production tasks and toward judgment, exception handling, coordination, stakeholder context, and accountable use of AI tools.
Labor-market context and wage risk
Median wage: $328,730 (May 2025, US national). Employment context: Visual-diagnosis specialty at the center of skin-AI screening. Typical education: Doctoral degree plus dermatology residency and board certification.
Wage vulnerability is 22, while transition feasibility is 62. A high wage-vulnerability score means workers should pay close attention to salary preservation before making a move. A high transition-feasibility score means there are adjacent paths that can reuse existing skills without requiring a complete career reset.
- Low displacement pressure
- Screening AI triages, does not treat
- Procedural volume anchors the specialty
Upskilling priorities
Skills that make this role more resilient
The safest upskilling plan starts with skills already close to the work. For Dermatologists, the strongest near-term skill priorities are listed below. These are useful whether the goal is to stay in the role, move to a redesigned version of the role, or transition into an adjacent occupation.
Visual diagnosis
Build proof of this skill through a work sample, checklist, dashboard, case note, workflow map, or portfolio artifact tied to the transition paths on this page.
Surgical technique
Build proof of this skill through a work sample, checklist, dashboard, case note, workflow map, or portfolio artifact tied to the transition paths on this page.
AI screening review
Build proof of this skill through a work sample, checklist, dashboard, case note, workflow map, or portfolio artifact tied to the transition paths on this page.
Patient counseling
Build proof of this skill through a work sample, checklist, dashboard, case note, workflow map, or portfolio artifact tied to the transition paths on this page.
90-day transition plan
The most practical next step is not to wait for a layoff or a full role redesign. Use the next 90 days to create evidence that you can operate in a safer, more AI-augmented version of the work.
- In the first 30 days, document the repetitive tasks in your current work and identify where AI can reduce drafting, lookup, classification, or reporting time.
- By 60 days, complete one small project connected to Mohs Surgeon, such as complete a surgical fellowship.
- By 90 days, compare internal openings and external postings for Mohs Surgeon or Teledermatology Program Lead and update your resume around measurable workflow outcomes.
FAQ
Questions about AI and Dermatologists
Will AI replace Dermatologists?
AI skin-image classifiers match specialists on narrow lesion benchmarks — the famous case study. Full-body exams, biopsy and surgery decisions, cosmetic procedures, and atypical presentations keep dermatologists diagnostically accountable. The better planning signal is not full replacement, but which tasks become automated, which tasks become AI-assisted, and which responsibilities still need human judgment.
Which parts of Dermatologists work are most exposed to AI?
Examine skin and diagnose conditions and Prescribe and administer treatments show the strongest automation pressure in this model. Examine skin and diagnose conditions and Prescribe and administer treatments are better treated as AI-augmented work.
What should Dermatologists learn next?
Start with Visual diagnosis, Surgical technique, AI screening review. The most practical adjacent paths in this model are Mohs Surgeon and Teledermatology Program Lead.
How should this score be used?
Use it as a planning signal, not a prediction. Confirm local hiring demand, wages, licensing, credentials, and employer adoption before making a career move.
Sources
Evidence trail
International Labour Organization / 20 May 2025Generative AI and Jobs: A Refined Global Index of Occupational ExposureOECD / 11 July 2023OECD Employment Outlook 2023: Artificial Intelligence and the Labour MarketO*NET Resource Center / Database 31.0 (August 2026)Occupation, Task, Skill, and Work-Activity DataUS Bureau of Labor Statistics / May 2025 estimates (released 15 May 2026)Occupational Employment and Wage Statistics