Fixing Inconsistent Remote Skincare Diagnosis with AI-Powered Image and Clinical Triage

The Problem

A skincare and dermatology provider relied on remote photo submissions to assess conditions, but the process was unreliable. Low-quality images, inconsistent triage decisions, and high variation in clinician assessment led to misclassification, unnecessary in-clinic visits, and avoidable workload on dermatologists. Clinicians routinely spent time on routine or low-risk cases that could have been triaged earlier with better quality and structure. The organisation needed a safer, more accurate way to assess conditions remotely without compromising clinical oversight.

The Solution

One of our digital squads developed an image-and-questionnaire triage agent co-designed with practising dermatologists. The system combined a clinical-grade computer vision model trained on a curated, consented dataset with a structured symptom questionnaire to capture context reliably. Triage decisions were supported by a RAG-backed explanation layer referencing clinical guidelines and evidence sources.

Clear escalation rules ensured that red-flag cases were always routed to a clinician via a dedicated review workflow. A clinician dashboard presented pre-populated briefs—including image analysis, symptom data, condition likelihoods, and risk indicators—helping specialists review and sign off with greater speed and confidence.

The Impact

Improved triage accuracy for urgent cases, increasing clinical confidence in remote assessment
25–40% reduction in unnecessary in-clinic appointments during pilot phases
Increased telehealth conversion and clinician throughput thanks to pre-populated briefing packs
Safer patient journeys through human-in-the-loop review for red-flag conditions