Models in this group take a clinical, dermoscopic or total-body photograph. Each table gives three hardware tiers — Minimum, the smallest configuration on which the model runs correctly; Medium, the usual production configuration; and High, a configuration sized for peak volume. The rate is what one server of that tier processes per hour. Use these figures for initial sizing only. Before production we benchmark your own data to confirm accuracy, latency, throughput and cost.
PanDerm
Vendor: Monash University / University of Oxford consortium
What it does: classifies across a wide disease list with risk stratification, and beat clinicians in reader studies on early melanoma detection while lifting non-specialist accuracy by about eleven points — the strongest case on this page for putting a model in a triage queue.
Beats clinicians in reader studies on early melanoma detection and improves non-specialist accuracy by about 11 percent (independent, Nature Medicine 2025).
PanDerm_Base
Vendor: PanDerm authors
What it does: is the embedding-only version: a linear probe on your own labelled set turns it into an endpoint in hours rather than weeks.
Task-dependent; downstream fine-tuning or linear probing required for specific tasks.
DermLIP_PanDerm
Vendor: PanDerm authors
What it does: pairs skin images with text, so lesions can be retrieved and labelled by description instead of by a fixed class list.
Task-dependent; evaluated on multiple dermatology classification and retrieval tasks.
Derm Foundation
Vendor: Google Health (Health AI Developer Foundations)
What it does: produces a 6144-dimension embedding covering 419 skin conditions, reaching clinician-comparable top-3 accuracy with small label budgets. Superseded by MedSigLIP and listed for continuity.
Supports 419 skin conditions; linear probes reach clinician-comparable top-3 accuracy with small label budgets (developer-reported). Legacy model — MedSigLIP is the current recommendation.
MONET
Vendor: Stanford University
What it does: scores dermatological concepts — asymmetry, border, pigment network — without concept-level labels, which is what makes a classifier output reviewable and a dataset auditable.
Concept annotation AUC about 0.87–0.94 for dermatological attributes without concept-level labels (independent, Nature Medicine).
ISIC-trained EfficientNet / ConvNeXt ensembles
Vendor: ISIC challenge community
What it does: grades melanoma at AUROC 0.93–0.96 on ISIC data. Accuracy drops markedly on darker skin tones and unfamiliar cameras, which is the first thing we measure rather than the last.
Melanoma AUROC 0.93–0.96 on ISIC held-out data; drops markedly on darker skin tones and out-of-distribution cameras (independent).
SkinGPT-4
Vendor: KAUST and community
What it does: discusses a photograph alongside a patient description and returns a differential in plain language. Correct in 60–79 percent of internal cases and explicitly not validated for triage.
Correct diagnosis category in about 60–79 percent of internal cases; not validated for triage (developer-reported).