Summary of A General-purpose Multimodal Foundation Model For Dermatology, by Siyuan Yan et al.
A General-Purpose Multimodal Foundation Model for Dermatology
by Siyuan Yan, Zhen Yu, Clare Primiero, Cristina Vico-Alonso, Zhonghua Wang, Litao Yang, Philipp Tschandl, Ming Hu, Gin Tan, Vincent Tang, Aik Beng Ng, David Powell, Paul Bonnington, Simon See, Monika Janda, Victoria Mar, Harald Kittler, H. Peter Soyer, Zongyuan Ge
First submitted to arxiv on: 19 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper introduces PanDerm, a multimodal dermatology foundation model that leverages self-supervised learning to tackle complex skin disease diagnosis and treatment. By training on over 2 million real-world images from 11 clinical institutions across 4 imaging modalities, PanDerm outperforms existing models in various clinical tasks, including skin cancer screening, phenotype assessment, diagnosis, segmentation, change monitoring, and metastasis prediction. State-of-the-art performance is achieved across all evaluated tasks, even when using only a fraction of labeled data. The model’s clinical utility is demonstrated through reader studies in real-world settings, outperforming clinicians by 10.2% in early-stage melanoma detection accuracy and enhancing diagnostic accuracy by 11%. PanDerm also exhibits robust performance across diverse demographic factors, including body locations, age groups, genders, and skin tones. This foundation model has the potential to accelerate AI integration in healthcare, particularly in dermatology. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper introduces a new way for computers to diagnose and treat skin diseases like cancer and eczema. It’s called PanDerm, and it uses artificial intelligence (AI) to look at lots of pictures of skin conditions. The AI is trained on over 2 million images from different hospitals and imaging machines, which helps it get really good at recognizing patterns and making diagnoses. In fact, it’s even better than doctors at some tasks! This could be a big help in medical care, because skin diseases can be tricky to diagnose and treat. PanDerm also works well for people of different ages, genders, and skin tones, which is important for treating patients from diverse backgrounds. |
Keywords
» Artificial intelligence » Self supervised
