A new tool, developed in Australia, is increasing the accuracy of skin cancer diagnosis by more than 10 percent.
The artificial intelligence (AI)-powered tool, created by an international team of researchers led by Melbourne’s Monash University, analyses multiple imaging types simultaneously, improving the detection of melanoma and a range of other skin diseases.
Associate Professor Zongyuan Ge said PanDerm was one of the first AI models built specifically to assist with real-world dermatological medical practice.
Associate Professor Ge said it analysed multiple types of images, including close-up photos, dermoscopic images, pathology slides, and total body photographs.
He said a series of evaluations showed PanDerm improved skin cancer diagnosis accuracy by 11 percent when used by doctors.
“The model helped non-dermatologist healthcare professionals improve diagnostic accuracy on various other skin conditions by 16.5 percent.”
Associate Professor Ge said it also showed the ability to detect skin cancer early, identifying concerning lesions before clinician detection.
He said the tool was trained on more than two million skin images and data for the model was sourced from 11 institutions in multiple countries, across four types of medical images.
“Existing AI models for dermatology remain limited to isolated tasks, such as diagnosing skin cancer from dermoscopic images; magnified images of skin captured using a dermoscope tool.
“Previous AI models have struggled to integrate and process various data types and imaging methods, reducing their usefulness to doctors in different real-world settings,” he said.
“PanDerm is a tool designed to work alongside clinicians, helping them interpret complex imaging data and make informed decisions with more confidence.”
Read the full research paper: A multimodal vision foundation model for clinical dermatology.
