Autonomous AI Helps Streamline Urgent Skin Cancer Referrals

In England, referrals have almost tripled since 2009, yet only about 6% lead to an urgent skin cancer diagnosis. This rising workload coincides with workforce shortages, with approximately one in four dermatologist roles in the U.K. unfilled. Against this backdrop, a new real-world study assessed whether autonomous artificial intelligence (AI) can reduce specialist time spent reviewing benign lesions.

The study evaluated a CE-marked Class III autonomous AI medical device in urgent suspected skin cancer pathways at two U.K. hospitals. The findings were presented at the European Academy of Dermatology and Venereology (EADV) Congress 2026. Following an initial validation period, the device used clinical and dermoscopic smartphone images to classify skin lesions. It discharged cases classified as benign without clinician review and referred higher-risk cases for teledermatologist assessment.

The prospective deployment involved 8,391 patients over 16 months, representing 94% of urgent suspected skin cancer referrals at the two hospitals. Overall, 86% of patients consented to autonomous decision-making. After exclusions, the device autonomously discharged 31% of patients at one hospital and 25% at the other. Teledermatologists discharged a further 24% and 25% of patients, respectively.

Compared with standard teledermatology, the autonomous pathway reduced the proportion of patients requiring routine follow-up from 27% to 12%. Its biopsy rate was 27%, compared with 43% for conventional face-to-face care. Researchers estimated that the pathway saved 2,851 clinician hours relative to a traditional face-to-face pathway, equivalent to an approximately 62% gain in clinical capacity. At 20 minutes per consultation, that time could accommodate more than 8,500 additional face-to-face appointments.

Ongoing safety monitoring identified six false-negative cases discharged from the pathway: five basal cell carcinomas and one melanoma in situ. Post-market surveillance detected these cases, and no adverse outcomes were identified within the available follow-up. In a national dataset that included both hospitals, sensitivity exceeded 98% for invasive melanoma, squamous cell carcinoma and basal cell carcinoma, while specificity was 72.1%.

“If these findings are replicated across larger populations and different health care settings, autonomous AI could become an important part of creating a more sustainable dermatology service—not by replacing dermatologists, but by allowing scarce specialist expertise to be focused where it can make the greatest difference to patients' lives,” said Dr. Lucy Thomas, lead author.

Related Links
European Academy of Dermatology and Venereology