LLM Clinical Trial Design Draft Automation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The existing process of clinical trial design is cumbersome and requires significant time and resources, leading to difficulties in communication between pharmaceutical companies and Contract Research Organizations (CROs), and necessitates a more efficient method for generating clinical trial designs.
Innovation Solution
A system utilizing a large language model (LLM) to automatically generate a draft of a clinical trial design by inputting clinical trial data and basic information, which includes training the LLM with a dataset of clinical trial information and using it to produce a draft report with structured sections.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual clinical trial design process is used, then comprehensive and accurate trial design can be achieved, but significant time and resources are required
Solution Approach 1:
A large language model serves as an intermediary between the input clinical trial parameters and the final trial design document. The LLM processes basic trial information (disease, drug, phase) and generates comprehensive trial design content, acting as a bridge that automates the design process while maintaining quality standards.
Solution Approach 2:
The manual mechanical process of clinical trial design by experts is replaced with an automated AI-based system. The LLM substitutes the human expert system, automatically generating trial design documents based on input parameters, thereby reducing time consumption while maintaining comprehensive coverage of trial requirements.
2Reliability
If expert communication and manual drafting are used, then high-quality clinical trial designs are produced, but the process is cumbersome and resource-intensive
Solution Approach 1:
The system enables self-service generation of clinical trial designs. By inputting basic trial parameters (disease name, drug information, trial phase), users can automatically generate comprehensive trial design documents without requiring extensive expert intervention or complex manual processes. The LLM handles the complex drafting work autonomously.
Solution Approach 2:
The LLM-based system provides universal functionality for generating various types of clinical trial designs across different diseases, drugs, and trial phases. A single automated system replaces multiple specialized manual processes, handling protocol development, inclusion/exclusion criteria, and trial methodology sections uniformly across diverse trial types.
3Measurement precision
If traditional clinical trial design methods are used, then thorough review and validation can be performed, but communication difficulties between pharmaceutical companies and CROs persist
Solution Approach 1:
The automated LLM system acts as a common intermediary platform that both pharmaceutical companies and CROs can use. It generates standardized trial design documents that maintain thoroughness while improving communication efficiency, providing a shared language and format that reduces misunderstandings between parties.
Solution Approach 2:
The system changes the parameters of trial design documentation by generating structured, standardized outputs with consistent formatting and comprehensive coverage. This standardization maintains thoroughness while making the collaboration process easier, as both companies and CROs work with uniformly structured documents that reduce communication friction.
Data Source
AI summary
An embodiment relates to a method for automatically generating a draft of a clinical trial design based on a large language model (LLM), which is performed by a server, comprising: (a) inputting a plurality of pieces of clinical trial data to a predetermined LLM as training data and training the LLM; (b) receiving, from a user device, basic clinical trial information including a clinical trial title, a drug name, formulation, a target disease, and a phase of a clinical trial to be conducted; (c) combining a plurality of pieces of pre-stored query text with the basic clinical trial information to generate a plurality of pieces of final query text; and (d) inputting the plurality of pieces of final query text to the LLM to generate a clinical trial design draft report in which a plurality of response information strings is output for a plurality of sections, respectively.