Understanding Large Language Models (LLMs) and Their Limitations
A Large Language Model (LLM), such as ChatGPT, is an extraordinary leap forward in AI, trained to understand and generate human-like text. These models are widely recognized for their capabilities. These are considered as cornerstone of modern automation and productivity.
Yet even the most powerful LLMs come with limitations. Although they can generate content based on their training, they are fundamentally disconnected from real-time, proprietary, or domain-specific data. This often results in generic or boilerplate responses, or worse, hallucinations—factually incorrect yet plausible-sounding information. Without access to specific and current data, LLMs fall short in delivering the precision and accuracy that most use cases in clinical trials demand.
Another key limitation is the context window—the amount of information an LLM can process at any given time. In clinical trials, where complex datasets, protocols, and patient data must be considered holistically, this limited window often leads to suboptimal outputs. LLMs simply cannot consider all necessary information in a single interaction, leading to missed details and incomplete results.
Fine-tuning—the process of training a pre-existing model on specific datasets to adapt it to a particular domain or task—has been proposed as a solution. While this can certainly help tailor an LLM to handle specific use case, it has significant drawbacks. Fine-tuning is not always practical due to lack of training data or scalable because clinical trials are dynamic and constantly evolving. Updating the model requires retraining every time new data is available, which is resource-intensive and slow. Fine-tuning falls short where real-time, contextual information is key.
Enhancing LLMs with Retrieval-Augmented Generation (RAG)
This is where the game changes with Retrieval-Augmented Generation (RAG) – anapproach to improve the efficacy of large language model (LLM) applications by leveraging specific, task-relevant data. This is done by retrieving data/documents relevant to a question or task and providing them as context for the LLM.
By seamlessly integrating a retrieval system with LLMs, RAG can access relevant, real-time data from external sources like databases, proprietary documents, or current clinical guidelines. With RAG, LLMs not only generate content based on their training but also pull in specific, up-to-date information that enhances the relevance and accuracy of their output.
In clinical trials, this means that instead of relying on general knowledge available on the web, the model can retrieve specific trial protocols, patient outcomes, or updated regulatory requirements. The model’s ability to dynamically fetch relevant data makes the generated response or analysis much more precise.
Embeddings and Data Retrieval
At the heart of LLMs and RAG is embeddings—vectorized representations that capture the meaning and relationships between words or phrases. In this space, cosine similarity is used to measure how similar two embeddings are, which is crucial when retrieving the most relevant data for a given query.
By comparing the embeddings of an input query to those of external documents, RAG ensures that the retrieved information is not only related but highly aligned with the task at hand. For clinical data management, this could mean accurate auto encoding or for medical writers finding the right content from historical documents for precise document generation.
Use Cases in Clinical Trials
The impact of RAG in clinical trials is profound. Here are some scenarios where this hybrid model can revolutionize operations:
Study Protocol Generation: When a clinical team needs to generate a study protocol, RAG can retrieve real-time data on the latest regulatory guidelines, and past protocols, ensuring the output is comprehensive, accurate, and compliant with the latest standards.
Data Monitoring and Adverse Event Reporting: As patient data streams in, RAG can retrieve specific, relevant historical data or similar cases, allowing real-time decision-making on adverse events. This leads to more informed, faster responses to critical trial issues, enhancing patient safety and trial efficiency.
Regulatory Submissions: LLMs with RAG can automate the creation of submission documents by retrieving the most recent regulatory requirements and aligning them with the trial’s specific data. This not only reduces manual effort but ensures compliance and accuracy in every submission.
Conclusion
The future of automating clinical trial workflows lies in augmented intelligence—leveraging AI tools that can dynamically retrieve and apply real-time, relevant information. LLMs alone, while powerful, are not equipped to handle the complexity and specificity that clinical trials demand. Retrieval-Augmented Generation (RAG) provides the missing link, empowering LLMs to operate with the precision, accuracy, and context required in this field.
There are more than 7,000 rare and undiagnosed diseases globally. Although each condition occurs in a small number of individuals, collectively these diseases exert a staggering human and economic toll because they affect some 300 million people worldwide.
Understanding Large Language Models (LLMs) and Their Limitations
A Large Language Model (LLM), such as ChatGPT, is an extraordinary leap forward in AI, trained to understand and generate human-like text. These models are widely recognized for their capabilities. These are considered as cornerstone of modern automation and productivity.
Yet even the most powerful LLMs come with limitations. Although they can generate content based on their training, they are fundamentally disconnected from real-time, proprietary, or domain-specific data. This often results in generic or boilerplate responses, or worse, hallucinations—factually incorrect yet plausible-sounding information. Without access to specific and current data, LLMs fall short in delivering the precision and accuracy that most use cases in clinical trials demand.
Another key limitation is the context window—the amount of information an LLM can process at any given time. In clinical trials, where complex datasets, protocols, and patient data must be considered holistically, this limited window often leads to suboptimal outputs. LLMs simply cannot consider all necessary information in a single interaction, leading to missed details and incomplete results.
Fine-tuning—the process of training a pre-existing model on specific datasets to adapt it to a particular domain or task—has been proposed as a solution. While this can certainly help tailor an LLM to handle specific use case, it has significant drawbacks. Fine-tuning is not always practical due to lack of training data or scalable because clinical trials are dynamic and constantly evolving. Updating the model requires retraining every time new data is available, which is resource-intensive and slow. Fine-tuning falls short where real-time, contextual information is key.
Enhancing LLMs with Retrieval-Augmented Generation (RAG)
This is where the game changes with Retrieval-Augmented Generation (RAG) – an approach to improve the efficacy of large language model (LLM) applications by leveraging specific, task-relevant data. This is done by retrieving data/documents relevant to a question or task and providing them as context for the LLM.
By seamlessly integrating a retrieval system with LLMs, RAG can access relevant, real-time data from external sources like databases, proprietary documents, or current clinical guidelines. With RAG, LLMs not only generate content based on their training but also pull in specific, up-to-date information that enhances the relevance and accuracy of their output.
In clinical trials, this means that instead of relying on general knowledge available on the web, the model can retrieve specific trial protocols, patient outcomes, or updated regulatory requirements. The model’s ability to dynamically fetch relevant data makes the generated response or analysis much more precise.
Embeddings and Data Retrieval
At the heart of LLMs and RAG is embeddings—vectorized representations that capture the meaning and relationships between words or phrases. In this space, cosine similarity is used to measure how similar two embeddings are, which is crucial when retrieving the most relevant data for a given query.
By comparing the embeddings of an input query to those of external documents, RAG ensures that the retrieved information is not only related but highly aligned with the task at hand. For clinical data management, this could mean accurate auto encoding or for medical writers finding the right content from historical documents for precise document generation.
Use Cases in Clinical Trials
The impact of RAG in clinical trials is profound. Here are some scenarios where this hybrid model can revolutionize operations:
Conclusion
The future of automating clinical trial workflows lies in augmented intelligence—leveraging AI tools that can dynamically retrieve and apply real-time, relevant information. LLMs alone, while powerful, are not equipped to handle the complexity and specificity that clinical trials demand. Retrieval-Augmented Generation (RAG) provides the missing link, empowering LLMs to operate with the precision, accuracy, and context required in this field.
Here are few articles I liked this week:
Press Releases Archives – Deep6.ai
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There are more than 7,000 rare and undiagnosed diseases globally. Although each condition occurs in a small number of individuals, collectively these diseases exert a staggering human and economic toll because they affect some 300 million people worldwide.
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