Summary of Generative Ai in Medicine, by Divya Shanmugam et al.
Generative AI in Medicine
by Divya Shanmugam, Monica Agrawal, Rajiv Movva, Irene Y. Chen, Marzyeh Ghassemi, Maia Jacobs, Emma Pierson
First submitted to arxiv on: 13 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computers and Society (cs.CY); Human-Computer Interaction (cs.HC)
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 provides a comprehensive overview of generative AI use cases in medicine for various stakeholders. It discusses the capabilities of generative AI and its potential applications in clinical settings, patient care, clinical trials, research, and training. The authors also highlight the challenges that need to be addressed to realize this potential, including maintaining privacy and security, improving transparency and interpretability, upholding equity, and rigorously evaluating models. These challenges give rise to open research directions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper explores how generative AI can help in medicine. It talks about different ways it can be used by doctors, patients, people organizing clinical trials, researchers, and students. The authors also discuss the problems that need to be solved so this technology can be useful, like keeping information private and secure, making sure people understand what’s going on, being fair, and testing the models well. This leads to new areas of research. |
