Summary of Guardrails For Avoiding Harmful Medical Product Recommendations and Off-label Promotion in Generative Ai Models, by Daniel Lopez-martinez
Guardrails for avoiding harmful medical product recommendations and off-label promotion in generative AI models
by Daniel Lopez-Martinez
First submitted to arxiv on: 24 Jun 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- 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 This paper proposes a method to detect potentially harmful medical product recommendations generated by Generative AI (GenAI) models. These models have shown great capabilities in various medical tasks but can learn uses of products that haven’t been adequately evaluated for safety and efficacy, or approved by regulatory agencies. To mitigate this public health risk, the proposed approach identifies unvetted recommendations and demonstrates its effectiveness using a recent multimodal large language model. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps keep people safe by making sure medical devices are used correctly. It proposes a way to stop GenAI models from suggesting uses that haven’t been approved or checked for safety. These models can learn a lot, but they need guidance to avoid giving bad advice. The approach works with a special kind of AI model and shows it’s effective in finding unapproved recommendations. |
Keywords
* Artificial intelligence * Large language model
