Summary of The Need For Guardrails with Large Language Models in Medical Safety-critical Settings: An Artificial Intelligence Application in the Pharmacovigilance Ecosystem, by Joe B Hakim et al.
The Need for Guardrails with Large Language Models in Medical Safety-Critical Settings: An Artificial Intelligence Application in the Pharmacovigilance Ecosystem
by Joe B Hakim, Jeffery L Painter, Darmendra Ramcharran, Vijay Kara, Greg Powell, Paulina Sobczak, Chiho Sato, Andrew Bate, Andrew Beam
First submitted to arxiv on: 1 Jul 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Computers and Society (cs.CY); Machine Learning (cs.LG)
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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 Large language models (LLMs) are powerful tools that can perform specific tasks at scale. However, deploying LLMs in high-risk domains like drug safety poses unique challenges, particularly the risk of hallucination, where LLMs generate fabricated information. This is concerning in settings where inaccuracies could harm patients. To mitigate these risks, we developed and demonstrated a proof-of-concept suite of guardrails designed to prevent certain types of hallucinations and errors in drug safety, potentially applicable to other medical safety-critical contexts. These guardrails include mechanisms for detecting anomalous documents, identifying incorrect drug names or adverse event terms, and conveying uncertainty in generated content. We integrated these guardrails with an LLM fine-tuned for text-to-text tasks, which involves converting structured and unstructured data within adverse event reports into natural language. This method was applied to translate individual case safety reports, demonstrating effective application in a pharmacovigilance processing task. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models can do lots of things, but they’re not perfect. When we use them for important tasks like drug safety, we need to make sure they don’t create fake information that could hurt people. We developed special tools called guardrails to help prevent this from happening. These tools look for weird documents, fix mistakes in drug names or bad reactions, and tell us when the model is unsure about what it’s saying. We used these guardrails with a special language model that can convert complicated data into easy-to-understand language. This helped us translate important reports about patient safety, which is crucial for making sure people stay healthy. |
Keywords
* Artificial intelligence * Hallucination * Language model
