On-Demand Clinical Score: 3.25/5.0

Dermatology Triage

On-Demand Knowledge Work | External audience

The Problem

Dermatology has severe access barriers: wait times exceed 30 days in many regions; benign skin lesions clog referral pipelines, delaying evaluation of potentially malignant lesions. AI-powered preliminary classification of patient-submitted lesion photos can triage urgency and reduce unnecessary referrals, improving access for high-risk lesions.

What the Agent Does

Patient or PCP submits photo of skin lesion via patient portal or secure messaging
Agent performs preliminary classification using computer vision: analyzes lesion appearance (size, border irregularity, color variation, surface characteristics)
Applies ABCDE criteria (Asymmetry, Border irregularity, Color variation, Diameter >6mm, Elevation/Evolution) and machine learning model trained on melanoma vs. benign lesions
Generates triage category: "Routine referral (likely benign mole)" vs. "Urgent referral (suspicious for melanoma; recommend evaluation within 2 weeks)"
DOES NOT diagnose; explicitly disclaims "This is a preliminary classification only; a dermatologist must evaluate"
Provides patient with guidance: if urgent, directs to fastest-available dermatology appointment or urgent care
Routes referral to dermatology with triage flag; urgent cases prioritized
Dermatologist reviews photo + triage classification and makes final determination

Data Requirements

Primary Data Sources: Patient-submitted lesion photos, patient demographics (age, skin type, family history of melanoma), ABCDE assessment criteria, computer vision model trained on melanoma vs. benign lesions
Classification: PHI/PII (patient identifiers, lesion photos)
Quality Metrics: Sensitivity for melanoma >90% (capture true high-risk lesions); specificity >70% (avoid false positives); triage accuracy validated against dermatologist assessment >85%
Complexity: Very High (medical image deep learning, melanoma classification, regulatory scrutiny)
Systems: Epic patient portal, DermEngine (dermatology AI platform)

Score Breakdown

Criterion	Weight	Score (1-5)	Weighted
Time Recaptured	15%	4	0.60
Error Reduction	10%	3	0.30
Cost Avoidance	10%	3	0.30
Strategic Leverage	5%	3	0.15
Data Availability	15%	3	0.45
Process Clarity	15%	3	0.45
Ease of Implementation	10%	3	0.30
Fallback Available	10%	3	0.30
Audience (Int/Ext)	10%	3	0.30
Composite	100%		3.25

Why It Scores Well

Access Improvement: Reduces dermatology referral burden by 30 to 40%; urgent cases prioritized
Patient Safety: Melanoma detection improves; early intervention improves outcomes
Regulatory Fit: Triage is classification, not diagnosis; FDA likely does not require premarket approval for triage-only tools; if tool recommends treatment, FDA review required
Scalability: Applies to all patients with suspicious skin lesions
Evidence Base: AI melanoma detection algorithms have achieved >90% sensitivity in published studies

Regulatory Alignment

FDA: Dermatology AI tools that provide "triage" (classify risk) may be exempt from premarket approval; tools that "diagnose" (recommend biopsy or treatment) typically require 510(k) review. Intended use statement critical.
State Medical Practice: Tool must not perform independent diagnosis; must be classified as clinical decision support
State Telemedicine Laws: Photo submission may trigger telemedicine licensing requirements depending on state and follow-up care plans
HIPAA: Lesion photos are PHI; must be encrypted and securely stored

Sprint Factory Fit

4 to 5 sprints (computer vision model training/validation, portal integration, triage workflow, FDA premarket pathway, clinical governance review)

Sprints Required: 4 to 5 sprints (computer vision model training/validation, portal integration, triage workflow, FDA premarket pathway, clinical governance review)
Timeline: 10 to 12 weeks from requirements to pilot; potentially 6 to 12 months if FDA 510(k) required
Governance: Dermatology medical director validation of triage accuracy in first 100 lesions; validate sensitivity >90% and specificity >70%
Success Metrics: Triage accuracy >85%; sensitivity >90%; dermatology wait times reduced >20%; urgent lesion detection rate >85%; patient satisfaction >4.0/5

Comparable Implementations

DermEngine: AI dermatology decision support
Research: Melanoma detection algorithms (>90% sensitivity published)
FDA 510(k) database: Skin lesion classification tools

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Governance Risks to Consider

Before deploying this use case, review these agentic AI risks from the Corvair Risk Catalogue. Each is scored on the DAMAGE framework and mapped to regulatory expectations.

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