News
AI-powered tool boosts skin cancer detection accuracy by 11 per cent, study finds
A new artificial intelligence tool has improved doctors’ ability to detect skin cancer by 11 per cent, according to research from an international team led by Monash University.
The system, named PanDerm, is designed to analyse multiple types of medical images simultaneously to help identify melanoma and other skin diseases more quickly and accurately than current methods.
With skin conditions now affecting 70 per cent of the global population, researchers say the technology could prove especially useful for early detection, which is crucial for better outcomes.
PanDerm was developed using more than two million skin images from 11 institutions across several countries.
It can analyse a wide range of visuals, including close-up photographs, dermoscopic images (taken with a magnifying tool), pathology slides, and total body photographs.
When tested, the tool helped non-dermatologist healthcare professionals improve diagnostic accuracy across a range of skin conditions by 16.5 per cent.
It also flagged suspicious lesions before they were identified by clinicians.
Associate Professor Zongyuan Ge is from Monash University’s Faculty of Information Technology.
The researcher said: “Previous AI tools have struggled to integrate and interpret multiple data types, limiting their usefulness in real-world clinical settings.
PanDerm is designed to work alongside clinicians, helping them interpret complex imaging and make decisions with greater confidence.”
Unlike traditional AI models designed for single tasks, PanDerm was evaluated across several clinical applications—including skin cancer screening, predicting recurrence risk, assessing skin type, mole counting, tracking lesion changes over time, lesion segmentation, and diagnosing a wide range of skin conditions.
In many cases, it delivered best-in-class results with just 5–10 per cent of the labelled data usually required.
Rather than replacing clinicians, PanDerm supports routine diagnostic workflows by processing image types commonly used in dermatology and providing diagnostic probability assessments.
This approach can enhance accuracy, particularly for non-specialists, and help detect subtle lesion changes over time.
First author Siyuan Yan is a PhD student in Monash University’s Faculty of Engineering.
Yan said: “By training PanDerm on diverse imaging modalities, we’ve created a system that mimics how dermatologists synthesise information from different visual sources.
“This allows for more holistic analysis than previous single-modality AI tools.”
Adjunct Professor Victoria Mar, director of the Alfred Health Victorian Melanoma Service, said the system could help identify subtle lesion changes and signs of future spread.
She added: “This kind of support could improve early detection and provide more consistent monitoring for melanoma patients.”
Diagnosis
Lung cancer drug shows breast cancer potential
Ovarian cancer cells quickly activate survival responses after PARP inhibitor treatment, and a lung cancer drug could help block this, research suggests.
PARP inhibitors are a common treatment for ovarian cancer, particularly in tumours with faulty DNA repair. They stop cancer cells fixing DNA damage, which leads to cell death, but many tumours later stop responding.
Researchers identified a way cancer cells may survive PARP inhibitor treatment from the outset, pointing to a potential way to block that response. A Mayo Clinic team found ovarian cancer cells rapidly switch on a pro-survival programme after exposure to PARP inhibitors. A key driver is FRA1, a transcription factor (a protein that turns genes on and off) that helps cancer cells adapt and avoid death.
The team then tested whether brigatinib, a drug approved for certain lung cancers, could block this response and boost the effect of PARP inhibitors. Brigatinib was chosen because it inhibits multiple signalling pathways involved in cancer cell survival.
In laboratory studies, combining brigatinib with a PARP inhibitor was more effective than either treatment alone. Notably, the effect was seen in cancer cells but not normal cells, suggesting a more targeted approach.
Brigatinib also appeared to act in an unexpected way. Rather than working through the usual DNA repair routes, it shut down two signalling molecules, FAK and EPHA2, that aggressive ovarian cancer cells rely on. FAK and EPHA2 are proteins that relay survival signals inside cells. Blocking both at once weakened the cells’ ability to adapt and resist treatment, making them more vulnerable to PARP inhibitors.
Tumours with higher levels of FAK and EPHA2 responded better to the drug combination. Other data link high levels of these molecules to more aggressive disease, pointing to potential benefit in harder-to-treat cases.
Arun Kanakkanthara, an oncology investigator at Mayo Clinic and a senior author of the study, said: “This work shows that drug resistance does not always emerge slowly over time; cancer cells can activate survival programmes very early after treatment begins.”
John Weroha, a medical oncologist at Mayo Clinic and a senior author of the study, said: “From a clinical perspective, resistance remains one of the biggest challenges in treating ovarian cancer. By combining mechanistic insights from Dr Kanakkanthara’s laboratory with my clinical experience, this preclinical work supports the strategy of targeting resistance early, before it has a chance to take hold. This strategy could improve patient outcomes.”
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