Autonomous AI Creates Dermatology Capacity in UK Hospitals: EADV 2026

AUTONOMOUS artificial intelligence (AI) could create capacity equivalent to more than 8,500 additional face-to-face dermatology appointments, according to a real-world study of 8,391 patients presented at EADV Congress 2026. The study evaluated autonomous AI triage within urgent skin cancer pathways across two UK hospitals over 16 months.

Following an initial validation period, a CE-marked Class III AI medical device was deployed across both sites. The system used clinical and dermoscopic smartphone images to classify skin lesions, autonomously discharging patients assessed as having benign lesions while referring higher-risk cases for teledermatologist review.

The 8,391 patients represented 94% of urgent suspected skin cancer referrals across the two hospitals. Overall, 86% of patients consented to autonomous decision-making.

Autonomous AI Reduced Clinical Workload

After exclusions, the AI autonomously discharged 31% of patients at one hospital and 25% at the other without clinician review. Teledermatologists subsequently discharged a further 24% and 25%, respectively.

The autonomous pathway reduced the proportion of patients requiring routine follow-up from 27% to 12% compared with standard teledermatology. Biopsy rates were also 27% compared with 43% for conventional face-to-face care.

Overall, the pathway was estimated to save 2,851 hours of clinician time compared with a traditional face-to-face pathway, representing an approximate 62% gain in clinical capacity. Based on 20-minute consultations, this equated to more than 8,500 additional face-to-face appointments during the 16-month study period.

Safety Monitoring Identified False Negatives

Safety monitoring was incorporated into the autonomous pathway. In a national dataset including both study sites, sensitivity exceeded 98% for invasive melanoma, squamous cell carcinoma (SCC), and basal cell carcinoma (BCC), with a specificity of 72.1%.

Six false-negative cases were discharged from the pathway and subsequently identified through post-market surveillance. These comprised five basal cell carcinomas and one melanoma in situ. No adverse outcomes were identified within the available follow-up.

The researchers emphasised that safe deployment requires ongoing monitoring to identify cases in which the system does not perform as expected and to learn from these events. They concluded that, if replicated across larger populations and different healthcare settings, autonomous AI could help create additional dermatology capacity while allowing specialist expertise to remain focused on patients requiring further assessment.

Reference

Thomas L et al. Autonomous AI triage in urgent skin cancer pathways: real-world safety, diagnostic performance and system impact in 8,391 patients. Poster 2827. EADV Congress, 30 September – 3 October, 2026.