A leading provider of digital and computational pathology solutions, Proscia released study results on new artificial intelligence (AI) technology that enables the detection of melanoma, the deadliest form of skin cancer and a leading cause of death worldwide. Taking these findings together, it is apparent that AI is capable of improving diagnosis, patient outcomes, and laboratory economics.

Julianna Ianni, Ph.D., Proscia’s Vice President of AI Research & Development, said, “Proscia’s technology represents a significant advancement in our work on skin pathology. Our AI not only identifies melanoma, a difficult diagnosis but also accounts for the high degree of variation in disease to push the boundaries of deep learning in medicine. In doing so, it holds great promise to help pathologists deliver faster, more consistent diagnoses and improve patient outcomes.”

Proscia is also conducting additional studies to demonstrate AI’s potential benefits in dermatopathology, including the following:

Improve patient outcomes –

An AI system that automatically detects melanoma flags high-risk cases for early diagnosis by alerting pathologists. Due to the growing number of skin biopsies, while pathologists are in short supply, these diagnoses could allow the most clinically relevant patients to be prioritized for treatment in an attempt to begin treatment sooner.

Assure consistency in diagnostics of difficult melanoma cases –

Melanomas are among the deadliest skin diseases, but they are also among the most difficult to diagnose, causing interobserver variability between pathologists. AI could assist the pathologist by distinguishing melanoma from mimics, increasing diagnoses, and enhancing patient outcomes.

Increasing laboratory productivity to improve profitability –

A skin biopsy in the United States may yield one of the hundreds of diagnoses, as 15 million are performed each year. The use of artificial intelligence to classify melanoma and non-melanoma skin cancer could allow laboratories to optimize the distribution of case volume between specialists and non-specialists, which would result in efficiency gains and partially compensate for declining reimbursements.