Benefits
- Benefit
-
Diagnosed 53.9% correctly against 43.8% without it
Awaiting review
- Outcome identifiers
-
Unresolved
Not reviewedUnresolved
- Category
-
Improved Task Accuracy
Awaiting review
- Status
-
Reported benefit
Awaiting review
- Beneficiary groups
-
People. Organizations.
Awaiting review
- Beneficiaries
-
Unresolved
Not reviewedUnresolved
- Reach
-
Unresolved
Not reviewedUnresolved
- Distribution and access
-
Unresolved
Not reviewedUnresolved
- Duration
-
Unresolved
Not reviewedUnresolved
- Expected?
-
Yes
Awaiting review
- Costs and harms
-
Unresolved
Not reviewedUnresolved
AI system
- System
-
Model Dermatology
Awaiting review
- Model
-
This is an explicit unresolved assignment: the sources on record do not settle this question.
Not reviewedUnresolved
- Release
-
Unresolved
Not reviewedUnresolved
- Provider
-
Unresolved
Not reviewedUnresolved
- Role
-
Unresolved
Not reviewedUnresolved
- Configuration
-
Unresolved
Not reviewedUnresolved
- Inputs and outputs
-
Unresolved
Not reviewedUnresolved
- Autonomy
-
Unresolved
Not reviewedUnresolved
- Workflow
-
Physicians saw the algorithm's suggestions in real time during the examination. Most of the gain came from physicians with little dermatology experience. Patients had Fitzpatrick skin types III or IV.
Awaiting review
- Human oversight
-
Unresolved
Not reviewedUnresolved
- Relation to AI
-
Patients were randomly assigned to examination with or without the algorithm, so the difference in accuracy is attributable to the algorithm.
Awaiting review
- AI contribution
-
Patients were randomly assigned to examination with or without the algorithm, so the difference in accuracy is attributable to the algorithm.
Awaiting review
Measurement
- Studies
-
Randomized. [2]
Fully reviewed: supporting details for Studies
Review date: 29 October 2026.
Sources: [2]
- Study design
-
Randomized. [2]
Fully reviewed: supporting details for Study design
Review date: 29 October 2026.
Sources: [2]
- Measurement
-
Top-1 diagnostic accuracy against final diagnosis. [2]
Fully reviewed: supporting details for Measurement
Review date: 29 October 2026.
Sources: [2]
- Setting and sample
-
576 consecutive adults with suspicious skin lesions at a tertiary hospital in Seoul; eight non-expert physicians. [2]
Fully reviewed: supporting details for Setting and sample
Review date: 29 October 2026.
Sources: [2]
- Measures
-
Diagnostic accuracy. [2]
Fully reviewed: supporting details for Measures
Review date: 29 October 2026.
Sources: [2]
- Comparison
-
Unaided examination. [2]
Fully reviewed: supporting details for Comparison
Review date: 29 October 2026.
Sources: [2]
- Results
-
53.9% vs 43.8% overall; 54.7% vs 30.7% for non-dermatology trainees. [2]
Fully reviewed: supporting details for Results
Review date: 29 October 2026.
Sources: [2]
- Additional value from AI
-
10.1 percentage points overall. [2]
Fully reviewed: supporting details for Additional value from AI
Review date: 29 October 2026.
Sources: [2]
- Limitations
-
Unmasked single-center design; no significant gain for dermatology residents; the developer is the first author. [2]
Fully reviewed: supporting details for Limitations
Review date: 29 October 2026.
Sources: [2]
Money
- Kind of value
-
The kind of monetary value is unclear.
Not reviewedUnresolved
- Amount
-
The monetary value is not established.
Not reviewedUnresolved
- Currency and prices
-
The currency and price basis are unclear.
Not reviewedUnresolved
- Period and unit
-
The period and unit of the monetary estimate are unclear.
Not reviewedUnresolved
- Realization
-
Whether the monetary value was realized is unclear.
Not reviewedUnresolved
- Recipients and payers
-
The recipients and payers are unclear.
Not reviewedUnresolved
- Gross or net
-
Whether the monetary figure includes costs is unclear.
Not reviewedUnresolved
- Included costs
-
The costs included in the estimate are unclear.
Not reviewedUnresolved
- Comparison
-
The financial comparison is not established.
Not reviewedUnresolved
- Method
-
The method used to calculate monetary value is unclear.
Not reviewedUnresolved
- Return and payback
-
The return on investment or payback period is not established.
Not reviewedUnresolved
People and places
- Participants
-
Developer: Seung Seog Han (I Dermatology Clinic and IDerma, Inc.). Operator: Department of Dermatology, Asan Medical Center, Ulsan University College of Medicine.
Awaiting review
- Jurisdictions
-
Benefit: Seoul,
South Korea. Developer: South Korea. Operator: South Korea.Awaiting review
- Ownership and control
-
Unresolved
Not reviewedUnresolved
- Dependencies
-
Unresolved
Not reviewedUnresolved
- Cross-border connections
-
Unresolved
Not reviewedUnresolved
- Geopolitical implications
-
The geopolitical implications are unclear.
Not reviewedUnresolved
Follow-up
- Conditions for replication
-
Users must be non-experts for whom the suggestions add information. The algorithm must be right often enough; wrong top suggestions lowered trainee accuracy.
Awaiting review
- Replication attempts
-
Unresolved
Not reviewedUnresolved
- Later findings
-
Unresolved
Not reviewedUnresolved
- Availability
-
Unresolved
Not reviewedUnresolved
- Institutional decision
-
A relevant institutional decision has not been established.
Not reviewedUnresolved
- Grounds
-
The grounds for an institutional decision are unclear.
Not reviewedUnresolved
- Action and result
-
The result of an institutional decision is not established.
Not reviewedUnresolved
- Success criterion
-
The criterion for a successful institutional decision is unclear.
Not reviewedUnresolved
Evidence
- Source numbers
-
AIBD-0649-source-0001–0003
Awaiting review
- Citations
-
Numbered references below.
Awaiting review
- Language
-
Unresolved
Not reviewedUnresolved
- Source relationships
-
Independent, Developer
Awaiting review
- Original evidence
-
Unresolved
Not reviewedUnresolved
- Access and versions
-
Unresolved
Not reviewedUnresolved
- Supported findings
-
Unresolved
Not reviewedUnresolved
- Evidence links
-
Unresolved
Not reviewedUnresolved
- Evidence strength
-
Independently studied. Sources: one independent peer-reviewed study, the makers' peer-reviewed study and the makers' other source. The cited evidence includes no finding by a regulator or court. [1] [2] [3]
- Missing evidence
-
Unresolved
Not reviewedUnresolved
Grades
- Assessed benefits
-
People. Organizations.
Awaiting review
- Depth
-
People: 2. Organizations: 2.
Awaiting review
- Reasons
-
People: Patients examined with the algorithm received a correct diagnosis in 53.9% of cases against 43.8% without it. Organizations: Non-dermatology trainees diagnosed 54.7% of lesions correctly with the algorithm against 30.7% without it.
Awaiting review
- Observed or intended
-
Unresolved
Not reviewedUnresolved
- Rubric
-
Unresolved
Not reviewedUnresolved
- Assessor and date
-
Unresolved
Not reviewedUnresolved
- Alternative assessments
-
Unresolved
Not reviewedUnresolved
- Fill
-
Independently studied
Awaiting review
Entry details
- Kind
-
The case describes an evaluation or a study. A single-center unmasked randomized controlled trial. [1]
Fully reviewed: supporting details for Kind
Review date: 29 October 2026.
Sources: [1]
- Related entries
-
No incident in the AI Incident Database snapshot of 26 October 2026 concerns the same kind of system harming the same kind of stakeholder.
Partly reviewedUnresolved: supporting details for Related entries
Compared with the incidents in the AI Incident Database snapshot of 26 October 2026.
- Field
-
Healthcare.
Awaiting review
- Purpose
-
Model Dermatology was built to suggest diagnoses for skin lesions from photographs. The higher diagnostic accuracy was that purpose.
Awaiting review
- Dates
-
Source published: 18 February 2022. Updated: 29 October 2026.
Awaiting review
- Stage
-
The system was used in a pilot. A single-center randomized trial in a tertiary hospital clinic. [1]
Fully reviewed: supporting details for Stage
Review date: 29 October 2026.
Sources: [1]
- Version
-
SHA-256: dc1fdbc0f7ec…
Awaiting review: supporting details for Version
SHA-256 of the public case, its assessments, and its displayed answers and notes, excluding its own identifier and the derived coverage summary.
References
- Other identifiers
-
Unresolved
Not reviewedUnresolved
- Category mappings
-
Unresolved
Not reviewedUnresolved
- Citable summary
-
Unresolved
Not reviewedUnresolved
- Revisions
-
1 revision. Latest: 29 October 2026.
Awaiting review
Sources
-
Peer-reviewed study Independent author
another study by Han et al. [ 40 ] showed that AI assistance mainly benefited non-dermatology trainees with the top accuracy of the AI-assisted group being significantly higher than that of unaided group, but there was no significant change in performance amongst dermatology residents
-
Peer-reviewed study Developer
Han SS, Kim YJ, Moon IJ, et al. Evaluation of Artificial Intelligence-Assisted Diagnosis of Skin Neoplasms: A Single-Center, Paralleled, Unmasked, Randomized Controlled Trial. J Invest Dermatol. 2022;142(9):2353-2362.e2. doi:10.1016/j.jid.2022.02.003
the accuracy of the AI-assisted group (n = 295, 53.9%) was found to be significantly higher than those of the unaided group (n = 281, 43.8%; P = 0.019)
-
Other Developer
Model Dermatology web service (build 2020). b2020.modelderm.com.
A dermatologist, Han Seung Seog has developed and maintained the service.