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

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.

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.