Summary of Advancing Multimodal Medical Capabilities Of Gemini, by Lin Yang et al.
Advancing Multimodal Medical Capabilities of Gemini
by Lin Yang, Shawn Xu, Andrew Sellergren, Timo Kohlberger, Yuchen Zhou, Ira Ktena, Atilla Kiraly, Faruk Ahmed, Farhad Hormozdiari, Tiam Jaroensri, Eric Wang, Ellery Wulczyn, Fayaz Jamil, Theo Guidroz, Chuck Lau, Siyuan Qiao, Yun Liu, Akshay Goel, Kendall Park, Arnav Agharwal, Nick George, Yang Wang, Ryutaro Tanno, David G. T. Barrett, Wei-Hung Weng, S. Sara Mahdavi, Khaled Saab, Tao Tu, Sreenivasa Raju Kalidindi, Mozziyar Etemadi, Jorge Cuadros, Gregory Sorensen, Yossi Matias, Katherine Chou, Greg Corrado, Joelle Barral, Shravya Shetty, David Fleet, S. M. Ali Eslami, Daniel Tse, Shruthi Prabhakara, Cory McLean, Dave Steiner, Rory Pilgrim, Christopher Kelly, Shekoofeh Azizi, Daniel Golden
First submitted to arxiv on: 6 May 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
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| 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 presents Med-Gemini, a family of multimodal models that inherit core capabilities from Gemini and are optimized for medical use via fine-tuning with various datasets. The models set new standards in AI-based report generation for chest X-ray reports, 3D computed tomography volumes, and other medical tasks. They also surpass previous best performances in visual question answering, classification, and polygenic risk prediction. The results highlight the potential of Med-Gemini across a wide range of medical tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper creates special AI models that can understand medical images and data better than usual. These models are trained on many different types of medical data, like X-rays and genomic information. They’re really good at generating reports for doctors and even beating the doctors themselves in some cases! The models also do well with other tasks like identifying pictures of skin or eyes. This could be very helpful in medicine, but more work needs to be done to make sure it’s safe and reliable. |
Keywords
» Artificial intelligence » Classification » Fine tuning » Gemini » Question answering
