Using AI to improve diagnosis of skin conditions

Picture this: a world where your doctor, with a few clicks, can catch skin cancer before it has a chance to dig in its claws. Thanks to a groundbreaking AI system called PanDerm, that world's getting closer. Developed by a global team led by Monash University and published in Nature Medicine, PanDerm doesn't just recognize skin problems — it sees them through multiple lenses, just like a seasoned dermatologist would.
"This kind of assistance could support earlier diagnosis and more consistent monitoring for patients at risk of melanoma," said Professor Victoria Mar, Director of the Alfred Health Victorian Melanoma Service. And that's not just wishful thinking. The numbers back it up.
How does it work?
PanDerm is no one-trick pony. Unlike earlier AI tools that could only read a single image type (think of a magnified shot under a dermoscope), PanDerm chews through all kinds of visuals:
- Close-up skin photos
- Dermoscopic images
- Pathology slides
- Full-body photographs
That's what they call a multimodal approach. Trained on more than two million images from 11 institutions across the globe, it doesn't just "look" — it analyzes, compares, and synthesizes, much like a human expert flipping between image types to connect the dots.
"By training PanDerm on diverse data from different imaging techniques, we've created a system that can understand skin conditions the way dermatologists do," explained Siyuan Yan, the lead author and PhD student at Monash University. It's this variety that helps PanDerm deliver a more holistic view of a patient's skin health.
In practice, it works like a smart assistant. Feed it your images, and it spits out a diagnostic probability — giving clinicians that extra nudge of confidence when making tricky calls.
Why does it matter?
Because speed and accuracy save lives. Skin cancer doesn't dawdle, and early detection is key. With 70% of the world dealing with some form of skin condition, tools like PanDerm are poised to be game changers.
Here's what's remarkable:
- 11% bump in accuracy for doctors diagnosing skin cancer with PanDerm.
- 16.5% improvement for non-specialists handling a range of skin issues.
- Detected problems before clinicians spotted them.
That's not just an upgrade — it's a leap.
"PanDerm is a tool designed to work alongside clinicians, helping them interpret complex imaging data and make informed decisions with more confidence," said Associate Professor Zongyuan Ge, one of the minds behind the project.
Even with limited data, PanDerm held its own. It showed top-tier performance using just 5-10% of the labeled data most models gobble up. That's a huge plus, especially in parts of the world where annotated medical data is scarce.
The context
Skin care isn't one-size-fits-all. Tools have to work across city hospitals, rural clinics, and everything in between. That's where PanDerm shines brightest. It was built for real-world messiness — not pristine lab conditions.
Professor H. Peter Soyer of the University of Queensland nailed it: "It could be particularly valuable in busy or resource-limited settings, or in primary care where access to dermatologists may be limited."
What makes PanDerm stand out isn't just its tech — it's the team behind it. From Australia to Austria, Singapore to Spain, this AI marvel came to life thanks to collaboration between Monash AIM for Health Lab and clinical partners across the globe.
Professor Harald Kittler from the Medical University of Vienna summed it up best: "Its ability to support diagnosis in varied real-world settings, including in Europe, is a step forward in making dermatological expertise more accessible and consistent worldwide."
PanDerm is still being fine-tuned before it hits clinic floors everywhere. But the road ahead is clear: smarter AI, broader testing, and a strong push for equitable care, no matter your zip code or skin type. And the goal is the future where your skin tells its story — and doctors finally hear it, loud and clear.
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