DermDx — software-only AI skin-cancer diagnosis, FDA-pending
Built a software-only AI for melanoma diagnosis exceeding the best-dermatologist baseline, then took it through the FDA submission process — the first of its kind. Underlying platform serves 3.5M patients across 40+ countries.
- Sensitivity
- 97%
- Specificity
- 67%
- Patients on platform
- 3.5M
- Countries deployed
- 40+
Problem
There was no FDA-cleared software-only AI for melanoma diagnosis. Existing approved devices (e.g. DermaSensor) were hardware-based and had specificity around 25%. Dermatologists were the gold standard at roughly 95% sensitivity / 60% specificity, but they don’t scale to 3.5M patients across 40+ countries — the platform we already operated.
The strategic question wasn’t "can we build a better model?" — it was: can we run a multi-year regulatory and clinical-data strategy in parallel with shipping a global imaging platform, without slowing either down?
Approach
I designed the work as two parallel tracks — a clinical + regulatory submission for DermDx, and continuous improvements to the underlying DermEngine imaging platform — sharing the same data, the same labeling pipeline, and the same engineering team.
- Anchor the clinical bar above dermatologists.
Set the model target above the best-dermatologist baseline (95% sensitivity / 60% specificity). Anything less wasn’t worth the regulatory cost. The FDA submission is a multi-year commitment — only worth making if the model genuinely beats the human gold standard.
- Field work first.
Traveled to Australian clinics — the world’s highest melanoma incidence per capita — to map the existing imaging workflow before proposing architectural changes. The redesign came out of a stopwatch and a notepad, not a PowerPoint.
- Use the platform as the data flywheel.
DermEngine already served 5,000 providers in 40+ countries — 17M images, 60% YoY image growth. The labeled dataset wasn’t the bottleneck — clinical-grade labeling was. Negotiated annotator contracts that scaled with image growth, and tracked annotator throughput / agreement / bias as product metrics.
- Run regulatory and product in lockstep.
FDA submission docs, clinical validation studies, and product roadmap shared the same source of truth so a model improvement could be reflected in submission packets within days — not quarters.
- Ship the platform improvements anyway.
While the FDA path ran, the platform doubled imaging throughput — patient imaging time fell from 2 hours to 20 minutes — which fed back into a better dataset for the AI work. Regulatory wasn’t allowed to block product velocity.
In regulated AI, owning the data and the workflow is a more durable moat than owning the model. The model gets caught up by the open-source frontier within a year. The data pipeline and the clinical relationships don’t.
What shipped
- DermDx model — 97% / 67%.
97% sensitivity / 67% specificity on the validation cohort — exceeds the best-dermatologist baseline on both axes (95% / 60%).
- FDA 510(k) submission.
Complete and pending. First software-only AI for skin-cancer diagnosis to be submitted.
- Imaging workflow — 2hr → 20min.
7-second per-lesion diagnosis loop. The clinical capacity unlock made the rollout pace possible.
- Platform-wide impact.
+40% DAU, +30% consults YoY, NPS rose to 70+ (from 50+ YoY) on the underlying DermEngine product.
- National integration playbook.
The IslaCare UK plan above became the template for landing DermDx + DermEngine inside national-scale clinical networks without the hospital re-doing its own imaging plumbing.
- Recognition.
1st place in a dermatology-focused hackathon during the build — concrete external validation while the FDA path was still pending.
Outcome
Clinical bar
Beats dermatologists
97% sensitivity / 67% specificity vs the 95% / 60% best-dermatologist baseline. Concrete numbers for peer-reviewed publication and FDA reviewers.
Regulatory
First software-only submission
First software-only AI for skin-cancer diagnosis through the FDA process. A category position dermatologists, payers, and platform operators were actively looking for.
Platform
100% provider retention
The platform retained near-100% of its 5,000 providers across 40+ countries through the multi-year work. The FDA narrative was an unlock, not a distraction.
Throughput
2hr → 20min imaging
Patient imaging time collapsed by 80%. 7-second per-lesion diagnosis. Clinic capacity unlock funded the next round of platform investment.
What I’d do differently
- Invest in synthetic data earlier. We were conservative with synthetic augmentation; I now think we left specificity points on the table.
- Engage FDA pre-sub conversations sooner. A 30-minute pre-submission meeting at month two would have saved a quarter of rework later.
- Treat the labeling vendor as a product, not a vendor. Annotator throughput, agreement, and bias were tracked as ops metrics — they should have been tracked as product metrics with a roadmap.
Related work
