Summary of Generative Ai For Synthetic Data Across Multiple Medical Modalities: a Systematic Review Of Recent Developments and Challenges, by Mahmoud Ibrahim et al.
Generative AI for Synthetic Data Across Multiple Medical Modalities: A Systematic Review of Recent Developments and Challenges
by Mahmoud Ibrahim, Yasmina Al Khalil, Sina Amirrajab, Chang Sun, Marcel Breeuwer, Josien Pluim, Bart Elen, Gokhan Ertaylan, Michel Dumontier
First submitted to arxiv on: 27 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 for medical data synthesis have seen significant advancements in the past few years. A new paper presents a comprehensive review of these models, covering various modalities including imaging, text, time-series, and tabular data. The study focuses on recent works from January 2021 to November 2023, excluding reviews and perspectives. This period highlights developments beyond GANs, which have been extensively covered previously. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Medical data synthesis with generative models is a growing area of research. Scientists are using these models to create fake medical images and text that can help doctors train and practice without risking patient safety. A new study looks at many different types of medical data and the generative models used to synthesize them. The researchers searched through recent scientific papers from 2021 to 2023 to find the latest advancements. |
Keywords
» Artificial intelligence » Time series
