Summary of Rapid Review Of Generative Ai in Smart Medical Applications, by Yuan Sun et al.
Rapid Review of Generative AI in Smart Medical Applications
by Yuan Sun, Jorge Ortiz
First submitted to arxiv on: 8 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 Generative models have revolutionized the application of artificial intelligence (AI) in healthcare by enhancing diagnostic speed and accuracy, improving medical service quality and efficiency while reducing equipment costs. The article explores their integration with intelligent medical devices, showcasing their potential to generate medical images, analyze data, and facilitate diagnosis. By combining generative models with Internet of Things (IoT) technology, real-time data analysis and predictions become possible, offering smarter healthcare services and supporting telemedicine. While challenges persist regarding computational demands, ethical concerns, and scenario-specific limitations, the promise of generative models in medical applications is undeniable. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Generative models are a type of artificial intelligence that helps doctors and hospitals make better decisions. They can create new medical images, analyze data, and even help diagnose patients faster and more accurately. This technology has huge potential to improve healthcare services, making them more efficient and cost-effective. By combining these models with the Internet of Things (IoT), we can get real-time updates and predictions, allowing for better care and support. There are some challenges to overcome, but overall, generative models have the power to transform healthcare. |
