AI Clinical Trial Protocol Retrieval With Predictive Data Augmentation

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Solution Overview

Problem

Conventional clinical trial design tools lack dynamic optimization, insufficient use of historical data, and inadequate capability for predicting trial metrics, leading to inefficient and manual protocol generation processes.

Innovation Solution

An AI-driven system that leverages generative artificial intelligence to analyze historical clinical trial protocols, providing real-time optimization and predictive analytics for generating and optimizing clinical study protocols, integrating with external APIs for enhanced decision-making.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If conventional manual methods are used for protocol generation, then medical professionals can write protocols with full control, but the process is inefficient and time-consuming

Engineering Contradiction:

Improveprotocol generation efficiencyVSAvoidtime for manual protocol writing

Core Design Contradiction:

ProductivityVSLoss of time

Solution Approach 1:

The patent replaces the manual mechanical process of protocol writing with an AI-based automated system. The AI assistant processes natural language inputs from medical professionals and automatically generates structured protocol documents, eliminating the need for manual drafting while preserving professional control through iterative refinement and approval mechanisms.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The AI assistant is designed to autonomously retrieve data from clinical trial databases, analyze requirements, and generate protocol content without requiring manual intervention for each step. The system serves itself by automatically querying databases, synthesizing information, and producing draft protocols that professionals can then review and approve.

Inventive Principle:

Principle #25Self-service

2Adaptability or versatility

If conventional trial design software is used, then data capture and management are supported, but the tools lack proactive protocol design capability and predictive analytics

Engineering Contradiction:

Improveprotocol design capabilityVSAvoidpredictive analytics and real-time metrics

Core Design Contradiction:

Adaptability or versatilityVSLoss of information

Solution Approach 1:

The AI assistant integrates with clinical trial databases to continuously retrieve updated information and provide real-time feedback on protocol design. The system analyzes historical data and current trial parameters to offer predictive analytics, suggesting optimizations and alerting professionals to potential issues before they arise during trial execution.

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The system performs preliminary analysis of protocol requirements by querying databases and analyzing historical trial data before final protocol creation. This advance preparation allows the AI to suggest optimal design parameters, predict potential challenges, and prepare draft protocols that incorporate lessons from previous trials.

Inventive Principle:

Principle #10Preliminary action

3Productivity

If AI tools like IBM Watson are used for patient matching, then recruitment is optimized, but comprehensive protocol design assistance is not provided

Engineering Contradiction:

Improvepatient recruitment efficiencyVSAvoidprotocol design support

Core Design Contradiction:

ProductivityVSEase of operation

Solution Approach 1:

The AI assistant is designed as a multi-functional system that performs both patient recruitment optimization and comprehensive protocol design assistance. It can analyze trial requirements, generate protocol documents, suggest design improvements, and coordinate patient matching—all within a single integrated platform, eliminating the need for separate specialized tools.

Inventive Principle:

Principle #6Universality (Multi-functionality)

4Loss of information

If static protocol repositories like clinicaltrials.gov are used, then reference data is available, but interactive protocol creation and predictive modeling are not supported

Engineering Contradiction:

Improveaccess to historical protocol dataVSAvoidinteractive protocol creation capability

Core Design Contradiction:

Loss of informationVSEase of operation

Solution Approach 1:

The AI assistant acts as an intermediary between static protocol repositories and medical professionals. It queries historical protocol data from databases like clinicaltrials.gov, processes this information through AI analysis, and presents interactive, actionable insights and draft protocols to users, transforming static reference data into dynamic design support.

Inventive Principle:

Principle #24Intermediary (Mediator)

Data Source

PatentUS20260018259A1Systems and methods for artificial intelligence - driven clinical trial protocol data retrieval and augmentation

Publication Date: 2026.01.15 MEDIDATA SOLUTIONS INC

AI summary

Disclosed are methods and systems for artificial intelligence-driven clinical trial protocol data retrieval and augmentation. A natural language user request is received via a user interface. The user request is combined with contextual data to produce a prompt for a large language model. The prompt is input to the model to produce a model response including a database query, in a database query language, and metadata. Clinical trial protocol data is retrieved from a first database based on the database query. Application programming interface (API) requests are generated based on the database query and/or the metadata. API calls are performed using the generated API requests to obtain clinical trial metrics. A response to the user request is generated based on the retrieved protocol data and the metrics.