Real-time AI Genome Processing - Powered by Groq

Peter introduces the use of Groq-powered LLMs in medical demonstrations, emphasizing the importance of accuracy and reliability in medicine. He discusses the limitations of generative LLMs due to their tendency to hallucinate information and the slow adoption in medical contexts. To address this, Peter explains the Retrieval Augmented Generation (RAG) technique, which enhances LLM output by incorporating additional information from a database of text sources. The demo showcases the use of a vector database containing 3000 medical genomics abstracts from pharmGKB, which provides clinical guidelines for gene-drug interactions. The first demo compares the LLM output with and without RAG, highlighting the increased detail and provision of working PubMed links in the RAG-enhanced output. The second demo demonstrates real-time genome annotation using LLM with RAG, processing a VCF file to identify gene variants and summarizing relevant abstracts with PubMed links. Peter concludes by affirming the potential of LLMs in medical settings when augmented with RAG for improved reliability and accuracy.

Takeaways

• 🚑 **Medical Context Reliability**: In medicine, it's crucial to have reliable and accurate information, which is a challenge with LLMs due to their tendency to hallucinate or fabricate data.
• 📚 **Slow Adoption of LLMs**: The slow uptake of generative LLMs in medicine is attributed to concerns about the accuracy of the information they provide.
• 🔍 **Referenced Information**: Medical professionals require referenced or sourced information to verify the data presented to them by LLMs.
• 🧠 **Retrieval Augmented Generation (RAG)**: RAG is a technique that enhances the accuracy and reliability of LLMs by incorporating additional context into the input query.
• 📊 **Database Integration**: RAG works by indexing a database of text sources and feeding the most relevant sources into the LLM to answer user queries.
• 🧬 **Medical Genomics Abstracts**: The demos utilize a vector database containing 3000 medical genomics abstracts sourced from pharmGKB, which provides guidelines on gene-drug interactions.
• 🔗 **Working Links**: Outputs with RAG include working PubMed links, allowing doctors to verify the provided information.
• 📈 **Detail and Accuracy**: RAG outputs are more detailed and accurate, including gene variants and genes that are often missed without RAG.
• ✅ **Customization for Medical Use**: The LLM can be customized for medical applications by using RAG to provide more detailed and verifiable responses.
• 🧬 **Real-Time Genome Annotation**: Using RAG and Groq hardware, the genome can be annotated in real-time, making it more understandable and actionable for medical professionals.
• 📝 **VCF Processing**: The VCF file, detailing a person's gene variants, is processed to match against a gene-drug database, providing summaries and PubMed links for each variant.
• 🏥 **Potential of LLMs in Medicine**: The demos demonstrate the potential for LLMs to be useful in medical settings when paired with techniques like RAG to improve reliability and accuracy.

Q & A

What is the main issue with using LLMs in a medical context?

The main issue is that LLMs often hallucinate or make up information, which is a significant problem for medical applications where reliability and accuracy are crucial.

Why has the uptake of generative LLMs in medicine been slow?

The slow uptake is due to the inherent inaccuracy and tendency of LLMs to generate false information, which is not acceptable in the medical field.

What is the role of referenced or sourced information in the medical context?

Referenced or sourced information is essential for doctors and medical professionals to verify the accuracy of the information and ensure it can be trusted.

What is RAG and how does it help with LLMs?

RAG, or retrieval augmented generation, is a technique that enhances the output accuracy and reliability of LLMs by providing additional context from a database of text sources, reducing hallucinations and allowing referenced responses.

How does the RAG process work?

RAG works by indexing a database of text sources when a user query comes in, gathering relevant sources, and feeding the top sources into the LLM, which then answers the query based on these sources.

What is the database used in the demos and what does it contain?

The database used is a vector database containing 3000 medical genomics abstracts sourced from pharmGKB, which includes clinical guidelines for gene-drug interactions.

What is the significance of providing working PubMed links in the output?

Working PubMed links allow doctors to verify and check the information provided by the LLM, ensuring that they can trust the source and the data's accuracy.

How does the LLM with RAG differ from the one without RAG in the first demo?

The LLM with RAG provides more detailed content, includes more gene variants, and offers working PubMed links for verification, whereas the LLM without RAG provides less detail and lacks some gene information.

What is the second demo about and how does it utilize LLM and RAG?

The second demo is about annotating the genome in real time using an LLM named Llama-2 70 billion, running on Groq hardware with RAG. It processes a VCF file to identify gene variants and uses the LLM to summarize relevant abstracts from the gene drug database, providing real-time, readable DNA annotations with working PubMed links.

What is VCF and why is it important in the context of the second demo?

VCF, or variant calling format, is a text file that describes the variants of each gene a person has. It is important because it provides the sequence genome used in the second demo to identify and annotate gene variants in real time.

How do these demos illustrate the potential of LLMs in a medical setting?

The demos show that with techniques like RAG, which improve reliability and accuracy, LLMs can be effectively utilized in a medical setting for tasks such as providing detailed genomics information and annotating genomes in real time.

What is the final message or takeaway from these demos?

The final message is that there is a place for LLMs in the medical field, but it requires enhancing their reliability and accuracy through techniques like RAG to ensure the information provided is trustworthy and actionable for medical professionals.

Outlines

00:00

🧬 Introduction to Medical Demos with LLMs

Peter introduces the video, highlighting the importance of reliability and accuracy in medical contexts. He discusses the limitations of LLMs, such as their tendency to hallucinate or fabricate information, which poses a significant challenge in medical applications. Peter emphasizes the need for referenced and sourced information in medicine, and introduces RAG (Retrieval-Augmented Generation) as a technique to improve LLM output accuracy by leveraging a database of text sources. The video will demonstrate the use of a vector database containing medical genomics abstracts from pharmGKB, a database with clinical guidelines on gene-drug interactions, to enhance LLM responses.

🔬 RAG in Action: Genomics Database Integration

The first demo showcases the application of RAG with a genomics database connected to an LLM named Mixtral, running on Groq hardware. A query regarding gene variants associated with adjusted warfarin doses is input, and the outputs with and without RAG are compared. The RAG-enhanced output is more detailed, includes missing gene variants, and provides working PubMed links for verification, demonstrating the customization of LLMs for medical use and the benefits of RAG in providing comprehensive and verifiable information.

🧬 Real-time Genome Annotation with LLMs and RAG

In the second demo, the process of annotating the genome using an LLM is discussed. The use of Llama-2, a 70 billion parameter model running on Groq hardware with RAG, is demonstrated to annotate the genome in real time. VCF (variant calling format) files, which detail a person's gene variants, are uploaded and processed to match against a gene-drug database. The LLM summarizes relevant abstracts and provides working PubMed links, effectively making someone's DNA readable in real time. This demo illustrates the potential of LLMs in medical settings when enhanced with techniques like RAG to improve reliability and accuracy.

Keywords

💡Groq

Groq is a company that specializes in creating AI hardware, specifically designed to accelerate machine learning tasks. In the context of the video, Groq powers the Large Language Models (LLMs) used for medical demos, emphasizing the importance of processing speed and efficiency for real-time applications in the medical field.

💡LLMs (Large Language Models)

Large Language Models (LLMs) are advanced AI systems designed to process and generate human-like language. They are used in various applications, including natural language understanding and generation. In the video, LLMs are crucial for medical demos, but their tendency to 'hallucinate' or create inaccurate information is a significant concern, especially in a medical context where accuracy is paramount.

💡Reliability and Accuracy

Reliability and accuracy are critical factors when using AI in medical applications. The video emphasizes the need for LLMs to provide trustworthy and precise information to medical professionals. This is particularly important because incorrect data can lead to serious consequences in healthcare.

💡Hallucinations

In the context of AI, 'hallucinations' refer to the generation of incorrect or made-up information by LLMs. The video discusses this as a significant issue, as it undermines the reliability of LLMs in medical applications where factual accuracy is essential.

💡RAG (Retrieval-Augmented Generation)

Retrieval-Augmented Generation (RAG) is a technique that enhances the accuracy and reliability of LLMs by incorporating additional information from a database of text sources into the model's input. This technique is highlighted in the video as a solution to reduce 'hallucinations' and provide referenced responses, which is particularly useful in the medical field.

💡Vector Database

A vector database is a type of database that uses vector representations to store and manage data. In the video, a vector database containing 3000 medical genomics abstracts is used to provide the LLM with relevant sources for generating responses, which is part of the RAG technique.

💡pharmGKB

pharmGKB is a comprehensive database that provides knowledge about the interactions between drugs and genes. It is used in the video as a source for the medical genomics abstracts within the vector database, which are then used to inform the LLM's responses.

💡VCF (Variant Calling Format)

The Variant Calling Format (VCF) is a file format used in genomics to store information about genetic variants. In the video, an example VCF file is uploaded to demonstrate how the LLM, with the help of RAG, can annotate the genome in real-time by searching for matches in the gene-drug database.

💡Annotation

In the context of genomics, annotation refers to the process of identifying and labeling parts of the genome, such as gene variants. The video showcases how LLMs, with the aid of RAG, can be used to annotate the genome by summarizing relevant abstracts and providing working PubMed links for verification.

💡PubMed

PubMed is a database of biomedical literature, providing access to published research articles. In the video, working PubMed links are provided in the output with RAG, allowing medical professionals to verify the information provided by the LLM, which is crucial for maintaining trust and reliability in medical applications.

💡Real-time Processing

Real-time processing refers to the ability of a system to handle and respond to input data as it is received, without significant delay. The video demonstrates the use of Groq hardware to enable real-time annotation of the genome using LLMs, which is essential for timely and efficient medical applications.

Highlights

Peter introduces medical demos using Groq powered LLMs (Large Language Models).

Reliability and accuracy are crucial in medical context, yet LLMs often hallucinate information.

The slow uptake of generative LLMs in medicine due to their inaccuracy.

Doctors require referenced or sourced information from LLMs for verification.

Introduction of RAG (Retrieval Augmented Generation) to enhance LLM output accuracy.

RAG reduces hallucinations and allows LLMs to provide referenced responses.

RAG works by indexing a database of text sources to gather relevant information for LLM input.

Demonstration of using a vector database containing 3000 medical genomics abstracts from pharmGKB.

The LLM Mixtral running on Groq hardware is used for the first demo.

Comparison of query outputs with and without RAG, showing more content and detail with RAG.

The RAG output includes working PubMed links for doctors to verify information.

Customizing LLMs for medical use by applying RAG to improve reliability and accuracy.

Using Llama-2 70 billion on Groq hardware with RAG to annotate the genome in real time.

VCF (variant calling format) is a sequence genome showing a person's gene variants.

Real-time processing of VCF files to match gene variants with the gene-drug database.

LLM is used to summarize gene abstracts and provide working PubMed links for each variant.

Groq enables real-time DNA readability by generating genome annotations.

LLMs have a place in the medical setting when enhanced with techniques like RAG for improved reliability.