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Dermatologists

Also called Skin doctor, Skin specialist, Dermatology physician, Dermatology doctor, Board-certified dermatologist

In brief

In May 2025, BLS counted 11,370 US dermatologists, with median pay of $328,730 a year. Of the BLS counts since 2021, the highest was 12,040, in May 2023. The earliest figure here is an estimate of 4,004 from 1970, not measured the same way as today's count. The last full technology era for dermatologists: Teledermatology, whole-slide digital pathology, and EHR integration, 2010 to 2020.

What the sources say

Official outlook

BLS projects employment to grow 6.8% from 2025 to 2035.

BLS Employment Projections 2025-35

AI exposure studies

Data as of BLS OEWS May 2025; BLS Employment Projections 2025-35. BLS publishes these counts as yearly survey snapshots and cautions against reading them as a precise trend.

Last updated .

Cite this page
APA

Future History. (2026, October 11). Dermatologists: jobs and pay since 1970 [Data set; data as of BLS OEWS May 2025; BLS Employment Projections 2025-35]. https://futurehistory.earth/timeline/29-1213

Chicago

Future History. "Dermatologists: jobs and pay since 1970." Data as of BLS OEWS May 2025; BLS Employment Projections 2025-35. Last modified October 11, 2026. https://futurehistory.earth/timeline/29-1213.

BibTeX

@misc{futurehistory_29_1213, author = {{Future History}}, title = {Dermatologists: jobs and pay since 1970}, year = {2026}, month = oct, howpublished = {\url{https://futurehistory.earth/timeline/29-1213}}, note = {Last updated 2026-10-11. Data as of BLS OEWS May 2025; BLS Employment Projections 2025-35}, }

The access date is today's date on your device.

drag to travel through time
1850187519001925195019752000now
2026
Known then as Cutaneous Physician / Skin Disease Specialist (dispensary era)
Known then as Dermatologist / Syphilologist (New York Dermatological Society era)
Known then as Dermatologist and Syphilologist (American Dermatological Association era)
Known then as Board-Certified Dermatologist and Syphilologist (American Board of Dermatology and Syphilology era)
Known then as Board-Certified Dermatologist (American Board of Dermatology, post-syphilology era)
Known then as Dermatologist / BLS SOC 29-1213
Known today as Dermatologist / BLS SOC 29-1213
Projected to still be called Dermatologist / BLS SOC 29-1213
Data from the May 2025 survey
US Employment
11K
Est.

Stated in source: BLS OEWS

Official statistic
Report an error
Median Annual Wage
$328,730
Est.

≈ $320,302 in 2024 prices (derived by us, CPI-adjusted)

Official statistic
Report an error
Beat · 1876

The American Dermatological Association was organized in 1876 as the first national dermatology society in the United States -- and among the first in the world -- seven years after the New York Dermatological Society had become the first local dermatological society globally in 1869. The ADA set membership limits at 100 that would constrain its scale for decades, eventually leading to the founding of the broader American Academy of Dermatology in 1938.

Beat · 1932

The American Board of Dermatology and Syphilology was incorporated in 1932, one of the four original boards that together founded the Advisory Board of Medical Specialties (the predecessor organization to ABMS). First examinations were administered in 1933: 20 physicians passed, 7 failed. From 1938 onward, all candidates were required to complete a three-year residency before sitting for the board exam -- a requirement that formalized the pipeline and limited specialty growth to the pace of residency slot expansion.

Beat · 1938

The American Academy of Dermatology and Syphilology was founded on January 14-15, 1938, at the Statler Hotel in Detroit with over 300 founding attendees. It was formed because the ADA's 100-person membership cap left more than 400 practicing dermatologists without a national home. Howard Fox of New York served as first president. The AAD later became the world's largest dermatology society and the primary platform for continuing education, clinical practice guidelines, and advocacy for the specialty.

Beat · 1970

Women comprised just 7% of the US dermatology workforce in 1970, reflecting the broader exclusion of women from medical residency training that persisted through the 1960s. By 2019, women had grown to 37% of practicing dermatologists (AMA masterfile); by 2020, women comprised approximately 47% of AAD members. Dermatology underwent one of the most rapid gender demographic shifts of any US medical specialty over this 50-year period.

Beat · 2002

The FDA's April 2002 approval of botulinum toxin (Botox) for cosmetic glabellar lines transformed the economic structure of dermatology practice. Cosmetic dermatology procedures that had required a plastic surgeon's operating room could now be delivered in a dermatologist's office in a 15-minute appointment. Combined with the approval of hyaluronic acid fillers in 2003, this created a cash-pay revenue stream that made cosmetic dermatology one of the fastest-growing practice segments of the early 2000s and pushed dermatology compensation into the top tier of physician specialties.

Beat · 2025

A 2025 survey published in Practical Dermatology found that 51.7% of dermatologists report using AI chatbots daily or weekly in their practice. Yet 97% manually edit AI responses before clinical use, and 92% cite privacy and accuracy limitations as concerns. The AAD's formal position statement frames AI as "augmented intelligence" -- designed to enhance, not replace, the physician-patient relationship. This professional consensus is notable: dermatology, despite having the largest AI device pipeline of any specialty, has positioned augmentation as the governing frame rather than replacement.

How to read this number

OEWS is a point-in-time survey snapshot, not a continuous time series; BLS advises against using it for year-over-year trend comparison.

Each dot is a cited figure over time; the dotted line only links them (values between aren't measured). Hollow dots are estimates.

Official statistic: Copied by a script from a statistics agency table or a published study's table, not retyped by hand. BLS figures and the forecast and exposure tables are also re-checked against that table on every data run. Researched context: Researched from the cited source rather than copied from it by a script, so it is not machine-checked yet. Spot a mistake? Each figure has a “Report an error” link.

Cited forecasts · present → 2035

Will AI replace dermatologists? What the research says

Each figure is stated by the named source; we publish no forecasts of our own. Where sources disagree, we show the disagreement rather than an average.

Each figure is stated by the named source; we publish no forecasts of our own. Where sources disagree, the spread is the honest signal.

What published forecasts say
Forecast change in the number of people doing this work, as published by each named source. Bars share one scale on every role, centred on zero and reaching the end at ±30%. Grey marks a small change, 3% or less either way.
BLS Employment Projections 2025-35
→ 2035
+6.8%
BLS National Employment Matrix, 2025-35 cycle, line for SOC 29-1213 (Dermatologists): 12.2 thousand jobs in 2025, projected 13.0 thousand in 2035, a change of +0.8 thousand (+6.8%). Matrix employment counts all jobs in the occupation, including self-employed workers (4% of this occupation in 2025). The average for all occupations in this cycle is +3.5%.
Official statisticReport an error
How exposed is this work to AI (research measures)
Scores as published by each study. Each row says what it measures. These measure what AI could technically do, not forecasts of jobs lost.
Eloundou et al. (2023), "GPTs are GPTs"
2023 study · Share of tasks exposed to LLMs (β, human-rated)
32.4%
of tasks
What this measures

Published occupation-level exposure from the paper's occ_level.csv for O*NET-SOC 29-1213.00 Dermatologists. Shown: human-rated β = 0.324, the share of the occupation's tasks an LLM could do in at least half the time at equal quality, counting tasks that need LLM-powered software at half weight (β = E1 + 0.5 × E2). Other published measures for this occupation: human-rated α (LLM alone) 0.176 and γ (all LLM-assisted) 0.471; GPT-4-rated β 0.382. This measures how much of the work an LLM could touch, not how many jobs disappear.

Official statisticReport an error
Today, in this role

What's changing day to day for dermatologists

Three parts of your work where AI is already doing real lifting, and what stays yours.

AI is sitting alongside you hereManage AI-driven prior authorization workflows for biologic therapies and specialty dermatology medications: using AI PA tools (LLM-based clinical note analysis) to auto-populate and submit authorization requests for dupilumab, secukinumab, adalimumab, and other high-cost biologics

Manage AI-driven prior authorization workflows for biologic therapies and specialty dermatology medications: using AI PA tools (LLM-based clinical note analysis) to auto-populate and submit authorization requests for dupilumab, secukinumab, adalimumab, and other high-cost biologics; reviewing and approving AI-drafted medical necessity narratives before submission; and authoring clinical appeals for AI-unable-to-handle complex or off-label cases.[9],[16],[17]

Where your edge is

Prior authorization is the leading administrative burden in dermatology: most specialty medications require PA, volumes at academic clinics have risen sharply, and dermatologists spend many hours per week on PA and claim denials (Adonis.io 2025). AI PA tools are beginning to automate the pattern-matching step, pulling clinical data from the note, mapping it to payer-specific criteria, and submitting complete requests. Your remaining value is in the high-complexity cases AI cannot handle: off-label biologic use with published evidence, PA appeals requiring peer-to-peer calls, and step-therapy override narratives. Build efficient templates for the most common PA types in your practice (dupilumab for atopic dermatitis, IL-17 inhibitors for psoriasis) to minimize time even on the residual manual cases.

AI is sitting alongside you hereReview and approve AI-drafted clinical encounter notes generated by ambient documentation AI (Dragon Copilot/DAX) following dermatology visits, verifying specialty-specific note content (lesion descriptors, biopsy indication, topical/systemic prescription rationale, follow-up interval) against the conversation transcript, correcting omissions or inaccuracies in skin examination findings, and signing the finalized note as the physician of record before EHR filing.

Review and approve AI-drafted clinical encounter notes generated by ambient documentation AI (Dragon Copilot/DAX) following dermatology visits, verifying specialty-specific note content (lesion descriptors, biopsy indication, topical/systemic prescription rationale, follow-up interval) against the conversation transcript, correcting omissions or inaccuracies in skin examination findings, and signing the finalized note as the physician of record before EHR filing.[6]

Where your edge is

Dermatology pilot data (several dermatologists, 2021-2023) showed Dragon Ambient eXperience (DAX) cut daily EHR time, a 22% reduction, and per-encounter documentation time. Provider note contribution dropped sharply as a share of the note's content. At 83.3% satisfaction, this is one of the highest-adoption AI tools in any specialty. Your review focus should target dermatology-specific failure modes: imprecise lesion descriptor language (color gradients, border irregularity), missing biopsy laterality and site, and incorrect topical formulation strength. The physician who treats the AI draft as a structured transcript rather than a dictation replacement captures the time savings without compromising note quality.

AI is sitting alongside you hereConduct full-body skin examinations augmented by AI dermoscopy: using DermEngine's AI-powered lesion tracking to compare serial dermoscopic images across visits, review AI-flagged changes in pigmented lesions against prior total-body photography maps, identify new or evolving lesions flagged by the change-detection algorithm, and apply licensed clinical judgment to determine biopsy, monitor, or discharge decisions for each flagged lesion.

Conduct full-body skin examinations augmented by AI dermoscopy: using DermEngine's AI-powered lesion tracking to compare serial dermoscopic images across visits, review AI-flagged changes in pigmented lesions against prior total-body photography maps, identify new or evolving lesions flagged by the change-detection algorithm, and apply licensed clinical judgment to determine biopsy, monitor, or discharge decisions for each flagged lesion.[18],[5]

Tools picking this up
Where your edge is

AI dermoscopy platforms like DermEngine are making total-body mole mapping far more actionable: serial AI-flagged change detection catches subtle evolution that the naked eye misses across visits. Your edge is that AI flags changes but cannot determine clinical significance: the same color change in a 25-year-old with fair skin and family history of melanoma carries a different biopsy threshold than in a 70-year-old with seborrheic keratoses. Build fluency with the specific error modes of AI dermoscopy tools (they over-flag benign Spitz nevi and seborrheic keratoses) so your review is faster and more calibrated.

Where dermatologists go next

Natural next steps for someone with your foundation: not exits, evolutions.

What each move takes

Dermatologists with depth in EHR configuration, AI tool evaluation, and teledermatology informatics are positioned for CMIO, VP of Clinical Informatics, or director of digital health roles. Dermatology's outsized AI device pipeline (several globally approved tools as of 2025) makes dermatologists with clinical AI expertise especially valuable for health system clinical informatics leadership. The 77% of physician informatics leaders who cited AI as their top priority in 2025 increasingly includes dermatologist backgrounds, given the specialty's high AI tool density. A dermatologist CMIO brings both the image AI literacy (dermoscopy validation, skin cancer detection algorithm assessment) and the procedural clinical context to bridge clinical and IT governance. Epic build/configuration familiarity; AI model evaluation methodology.

What you'd add
  • · Health informatics credentials: AMIA 10×10 certificate (online, 30+ hours) or Master of Biomedical Informatics; ABPM Clinical Informatics board certification (fellowship pathway)
  • · AI model evaluation methodology: understanding sensitivity/specificity tradeoffs, training data diversity auditing (especially Fitzpatrick skin type representation), post-market surveillance for FDA-cleared devices
  • · EHR platform expertise: Epic or Cerner build certification; teledermatology workflow configuration; AI module integration with PACS and dermatology imaging systems
  • · HL7 FHIR and dermatology imaging standards: DICOM for dermoscopy and whole-slide pathology imaging; interoperability between AI tools and EHR systems
  • · Healthcare cybersecurity and HIPAA compliance: PHI governance for AI systems processing skin images and pathology slides; HHS OCR risk analysis methodology
What it takesSome new skills to pick up
Tools of the era

How has the work of dermatologists changed since 1836?

The tools that defined the work, one line per era. Select an era to see how it reshaped the work.

  • Clinical morphology and bedside diagnosis (Willan classification era)

    The dermatologist's core tool from Bulkley's 1836 dispensary through the end of the 19th century was the trained eye. Robert Willan's 1808 morphological classification system -- describing skin conditions by their primary lesion type (macule, papule, vesicle, pustule, wheal, bulla) -- gave the specialty its diagnostic vocabulary. A dermatologist of this era carried a hand lens and relied on inspection under natural light, knowledge of disease patterns, and patient history to distinguish syphilitic eruptions, infectious dermatoses, and inflammatory conditions in a world without germ theory, patch testing, or biopsy. The Broome Street Infirmary and its successors collected patients in sufficient volume to make pattern recognition possible across hundreds of cases, a practice model that would later be called clinicopathological correlation.

  • Dermatoscope / diascope and skin histopathology (Unna 1893; Wood's lamp 1903)

    Paul Gerson Unna of Hamburg published his technique of "diaskopie" in 1893, using immersion oil with a glass lens to render the uppermost epidermal layers translucent and reveal the vascular and structural features of skin lesions below. This was the conceptual ancestor of the modern dermatoscope. In the same era, Unna's 1894 landmark text on cutaneous histopathology established a rigorous laboratory science for the specialty, and Robert Wood's ultraviolet lamp (introduced to dermatology around 1903) allowed fluorescence-based identification of fungal and bacterial skin infections. The Wood's lamp remained a workhorse diagnostic tool for tinea capitis and pityriasis versicolor well into the late 20th century. These tools moved dermatology from pure clinical observation into laboratory-augmented diagnosis, a transition that accelerated with the spread of hospital-based skin clinics in the early 20th century.

  • Mohs micrographic surgery and broadband UV phototherapy (Frederic Mohs 1936-1940; Goeckerman UVB 1923; widespread procedural dermatology)

    Frederic Mohs treated his first patient with microscopically controlled excision on June 30, 1936, and by 1940 had refined the technique into a systematic procedure using a zinc chloride paste to fix tissue in situ before staged removal. Mohs surgery transformed what dermatologists could offer patients with skin cancer on cosmetically sensitive sites: the nose, ears, eyelids, and lips where the margin-controlled approach dramatically improved cure rates while minimizing tissue sacrifice. Early Mohs was performed under local anesthesia in an outpatient office setting, foreshadowing the shift of procedural dermatology away from hospital operating rooms. Simultaneously, William Henry Goeckerman's 1923 protocol combining coal tar with broadband UVB became the standard treatment for severe psoriasis and anchored phototherapy as a procedural tool. These developments made dermatology increasingly procedure-heavy, a structural feature that would later resist AI displacement.

  • PUVA photochemotherapy, first lasers, and board certification standards (1974-1995)

    In 1974, John Parrish at Massachusetts General Hospital successfully introduced psoralen plus UVA (PUVA) photochemotherapy, combining 8-methoxypsoralen with a newly developed high-intensity UVA light source. PUVA proved highly effective for psoriasis, vitiligo, and cutaneous T-cell lymphoma, transforming dermatology offices into phototherapy centers with dedicated light boxes and specialized scheduling. In 1963, Leon Goldman had pioneered the pulsed ruby laser for selective destruction of pigmented lesions; by the 1970s-1980s argon and CO2 lasers were entering dermatology practice for port-wine stains, tattoo removal, and resurfacing. The American Board of Dermatology, which had administered its first exams in 1933, now required a three-year residency for board eligibility (enacted 1938), creating a standardized pipeline whose graduates were certified proceduralists as well as diagnosticians.

  • Digital dermoscopy, biological therapies, and cosmetic dermatology boom (botulinum toxin FDA 2002; biologics 2002-2008)

    The late 1990s and early 2000s transformed dermatology across three dimensions simultaneously. First, digital dermoscopy moved from research curiosity to clinical tool: videodermatoscopes allowed the capture, storage, and serial comparison of dermoscopic images, turning the visual subspecialty into a data-generating practice. Second, the biological therapy era arrived: etanercept received FDA approval for psoriatic arthritis in 2002 and psoriasis in 2004; adalimumab for psoriasis in 2008, ushering in a new practice model of managing patients on complex immunomodulatory agents with monthly monitoring and lab review. Third, the FDA approved botulinum toxin (Botox) for cosmetic use in April 2002, and hyaluronic acid fillers followed in 2003. The cosmetic dermatology market exploded: procedures that had required a plastic surgeon now belonged to a dermatologist's afternoon schedule, transforming the compensation structure of the specialty and making dermatology among the most lucrative residency matches in American medicine.

    Effect on the work

    The cosmetic dermatology boom drove compensation growth: median dermatologist starting salaries reached $420,000 by 2019, placing the specialty in the top six for starting compensation across all of US medicine.

  • Teledermatology, whole-slide digital pathology, and EHR integration (store-and-forward 2010s; digital pathology 2007+)

    Teledermatology -- pioneered during US military operations in Somalia in 1992 but mainstreamed in civilian practice only in the 2010s -- allowed dermatologists to review patient-submitted or clinician-captured photographs asynchronously (store-and-forward) without a real-time visit. Whole-slide imaging, commercially available since the late 1990s, began moving toward mainstream dermatopathology adoption after 2007, when digital pathology crossed an inflection point in processing speed and storage economics. The broader EHR adoption wave (accelerated by the HITECH Act of 2009) also reshaped dermatology documentation: practice management moved onto integrated platforms that combined scheduling, billing, and clinical notes, setting the stage for the ambient AI documentation tools that followed.

  • AI dermoscopy, ambient documentation AI, and FDA-cleared skin cancer detection devices (DermaSensor 2024; Dragon Copilot; 15+ approved AI tools globally)

    The 2020s brought the largest technology inflection in dermatology since the cosmetic boom of the early 2000s, this time driven by AI. Dragon Copilot (rebranded from Nuance DAX Copilot in March 2025) achieved a 22% reduction in daily EHR time in a dermatology-specific pilot study, cutting per-encounter documentation. DermaSensor received FDA clearance on January 17, 2024 as the first AI-powered medical device authorized to detect all three common skin cancers, achieving 96.5% sensitivity across melanoma, basal cell carcinoma, and squamous cell carcinoma. The AAD reported several regulatory-approved AI devices in dermatology globally by 2025, with the NICE Early Value Assessment recommending Skin Analytics DERM for NHS use in May 2025, citing its ability to halve referrals on the urgent skin cancer pathway while maintaining safety. Within the practice, AI prior authorization tools began automating a burden that consumed several hours per week per dermatologist (Adonis.io 2025). These tools augment rather than threaten the licensed procedural core: Mohs surgery, biopsies, cosmetic injectables, and laser procedures remain entirely hands-on.

    Effect on the work

    AI tools are expanding effective dermatologist capacity rather than reducing the headcount.

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Dermatologists employment and pay by year

On record since1836
Latest US employment11,370 (May 2025)Source: BLS OEWS May 2025Official statisticReport an error
Latest US median pay$328,730 (May 2025)Source: BLS OEWS May 2025Official statisticReport an error
Outlook+6.8% by 2035Source: BLS Employment Projections 2025-35Official statisticReport an error
View all 11 years of cited US data, each linked to its source
YearUS employmentAnnual pay (median unless marked)SourceReport
19704,004n/aEst., MDedge Dermatology Researched contextReport
1971n/a$45,000Est., MDedge Dermatology Researched contextReport
19804,372n/aEst., PMC 5470415 Researched contextReport
19925,996n/aEst., PMC 5470415 Researched contextReport
201610,845n/aEst., PMC 5470415 Researched contextReport
2019n/a$420,000Est., MDedge Dermatology Researched contextReport
20219,230n/aBLS OEWS May 2021 Official statisticReport
202211,640n/aBLS OEWS May 2022 Official statisticReport
202312,040n/aBLS OEWS May 2023 Official statisticReport
202410,080n/aBLS OEWS May 2024 Official statisticReport
202511,370$328,730BLS OEWS May 2025 Official statisticReport
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