Using RAG to Avoid Hallucinations in AI for Mass Tort Case Review

As legal professionals, your practice is rooted in facts and evidence. Embracing artificial intelligence (AI) in the context of mass tort medical record review can seem daunting, especially with concerns about the accuracy and reliability of AI-generated content. However, advancements in AI, specifically Retrieval-Augmented Generation (RAG), offer promising solutions. This article explores how RAG can enhance the efficiency and accuracy of legal reviews, drawing insights from the recent presentations from our own Joe Barrow, which summarizes the paper "Hallucination-Free? Assessing the Reliability of Leading AI Legal Research Tools" by Varun Magesh et al. from Stanford University.

Heads up, there is a lot of “AI Jargon” in this post. Hopefully this helps…

Understanding Retrieval-Augmented Generation (RAG)

Retrieval-Augmented Generation (RAG) is an AI approach that combines two powerful techniques: retrieval and generation. Large language models (LLMs) cannot reliably cite sources or reference documents, even if  trained on the document. This limitation can lead to "hallucinations," where the AI generates incorrect or fabricated information. Think of it like a librarian who makes up books and authors if they can’t find the needed reference. RAG attempts to address this by integrating a retrieval mechanism that fetches relevant documents and information before generating responses, ensuring the generated content is grounded in accurate and relevant data. One key takeaway from the paper is that RAG only gets you so far. You still end up with lots of hallucinations because the models don't really "reason."

The Goal of RAG in Legal Reviews 

1. The potential to reduce hallucinations: Legal professionals require precise and reliable information. RAG significantly reduces the risk of hallucinations by grounding AI-generated content in verified documents, such as medical records or legal precedents. This reliability is crucial in mass tort cases, where the accuracy of medical record reviews can significantly impact case outcomes.
2. Enhanced Efficiency: Reviewing vast medical records manually is time-consuming and prone to human error. RAG enhances efficiency by quickly retrieving and summarizing relevant information, allowing legal professionals to focus on analysis and decision-making rather than data collection.
3. Consistent Accuracy: RAG ensures consistency in the information presented, reducing the variability that can occur with manual reviews. This consistent accuracy is particularly important in legal contexts where uniformity in case reviews can influence the fairness and integrity of the judicial process.

Applications of RAG in Lexis+ AI by LexisNexis 

This platform leverages RAG to provide legal citations connected to source documents. Ensuring the information is derived from sources enhances the trustworthiness of the generated content.

Addressing Challenges with RAG 

Despite the promises, RAG has challenges. The paper by Magesh et al. identifies several key areas to be mindful of:

• Domain-Specific Modeling for Accuracy: Effective retrieval depends on accurately modeling the domain. This requires comprehensive training and continuous updates to the AI model to ensure it remains relevant and reliable.
• Balancing Automation and Human Oversight for Reliability: While RAG enhances efficiency, human oversight remains crucial. Combining AI and human expertise, known as centaur systems, often yields the best results. Legal professionals should use RAG to augment their capabilities, not replace them.
• Performance Measurement for Reliable Outputs: Accurate performance measurement is essential to assess the effectiveness of RAG systems. Legal professionals should establish benchmarks and metrics to evaluate the quality and accuracy of AI-generated content.

Empirical Evidence and Limitations

The paper by Magesh et al. provides a rigorous empirical evaluation of RAG-based AI tools in legal research. Their study found that while tools like Lexis+ AI and Westlaw AI-Assisted Research reduce hallucinations compared to general-purpose AI systems, hallucinations persist significantly. Specifically, Lexis+ AI had a hallucination rate of 17%, while Westlaw's rate was 33%. These findings highlight the need for continued vigilance and the integration of robust verification processes in legal practice.

Claims regarding Hallucinations from existing Legaltech solutions:

What do you need to take away? 

• You need to model your domain for retrieval (naive retrieval)
• Perfect retrieval still isn’t sufficient for perfect answers (reasoning error & sycophancy 
• Centaur systems > humans alone* >> llms+rag alone
• Measure system performance accurately, lest someone else does

Confidently Using AI in Medical Record Reviews 

Pattern Data is more like a cyborg paralegal system by integrating RAG technology with human oversight. This synergy allows for the benefits of AI, such as speed and accuracy, while mitigating risks like hallucinations through expert review. By adopting this approach, Pattern Data ensures that legal professionals can rely on AI-generated content without sacrificing the quality and reliability essential in legal practice.

Complementary Insights from Pattern Data Blogs

For those new to AI in legal contexts, here are some additional articles from our team: 

• Understanding Artificial Intelligence: A Primer for Mass Tort Attorneys offers a comprehensive overview of how AI, including RAG, fits into legal practice. 
• Jennifer Hoekstra on Navigating ESI and AI in Mass Torts provides practical insights on the intersection of electronic discovery and AI, which is highly relevant for handling large volumes of medical records.
• How OCR Can Miss Quality Cases and Why AI Can Do Better explains the limitations of traditional optical character recognition (OCR) systems and how AI technologies can improve accuracy and efficiency in document review.

Conclusion

At Pattern Data, we're committed to blending AI with human expertise to transform legal practices. Using Retrieval-Augmented Generation (RAG) technology alongside careful human review, we create a powerful "cyborg paralegal" system. This approach combines AI's speed and accuracy with expert oversight's reliability.

Using RAG, legal professionals can improve the accuracy, efficiency, and consistency of mass tort medical record reviews, leading to better case outcomes. Our method ensures that AI-generated content is trustworthy and up to the high standards required in legal work.

For a more detailed look at how this "cyborg paralegal" system works in practice, check out our blog post, Introducing the Cyborg Paralegal, which explores a real-world application of RAG technology in legal settings.

For a deeper understanding of RAG and its uses, we recommend Varun Magesh et al.'s paper "Hallucination-Free? Assessing the Reliability of Leading AI Legal Research Tools." 

By embracing RAG and combining AI with human skills, legal professionals can confidently handle complex mass tort cases and deliver top-notch service in a data-driven world.