LLM Clinical Trial Protocol Generation With Integrated Content Tools
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Solution Overview
Problem
Clinical trial document generation is labor-intensive and requires significant manual input, limiting efficiency and accuracy.
Innovation Solution
A computerized generative AI system using large language models (LLMs) with integrated content generation tools automates the creation of clinical trial documents, such as protocols and reports, by retrieving and generating information based on user input and predefined actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual input and preparation is used for clinical trial documents, then accuracy and control are maintained, but labor intensity and time consumption increase significantly
Solution Approach 1:
The system enables self-service document generation where the AI model automatically creates clinical trial documents based on input parameters without requiring extensive manual preparation. The system handles document generation autonomously, reducing the need for manual intervention while maintaining accuracy through structured output formats and validation mechanisms.
Solution Approach 2:
The patent replaces manual mechanical document preparation with an automated AI-based system. The LLM substitutes human writers and preparers, automatically generating documents based on input data and parameters, thereby eliminating the time-consuming manual process while maintaining document quality and accuracy.
2Productivity
If automated AI generation is used, then productivity and time efficiency improve, but system complexity and computational resources increase
Solution Approach 1:
The system employs a universal AI model that can generate multiple types of clinical trial documents (protocols, informed consent forms, study reports) through a single integrated platform. This multi-functionality reduces overall system complexity by consolidating what would otherwise require multiple separate tools and processes into one unified system.
Solution Approach 2:
The patent utilizes parameter changes in the AI model's output configuration to adapt to different document types and requirements. By adjusting parameters such as document structure, tone, and content focus, the system can generate various clinical trial documents without requiring complex reconfiguration, thereby managing system complexity while maintaining high productivity.
3Manufacturing precision
If comprehensive document generation is implemented, then accuracy and completeness improve, but manual intervention and review requirements increase
Solution Approach 1:
The system incorporates feedback mechanisms where generated documents are reviewed and validated through automated checks and can be iteratively refined based on user feedback. This feedback loop ensures high accuracy and completeness of documents while minimizing manual intervention by automatically incorporating corrections and improvements without requiring extensive manual rewriting.
Data Source
AI summary
In an example method, a computer system accesses first data representing a plurality of first clinical trial protocols and second data representing a plurality of content generation tools. The system receives a user input instructing the computer system to generate a second clinical trial protocol using an LLM, where the user input includes an indication of a subject of the second clinical trial protocol. The system determines a plurality of actions to generate the second clinical trial protocol, and determines one or more content generation tools associated with each of the actions. The system causes the LLM to perform each of the actions using the one or more content generation tools associated with that action and based on the first data. Further, the system generates the second clinical trial protocol based on an output of the LLM, and stores a data structure representing the first second clinical trial protocol.