AI in Clinical Trials

a brief description of conference speech

Stages for patient

  • Enrollment
  • Participation
  • Analysis

AI application

Research

  • Predicting patterns in historical data
  • Hypotheses using articles, data, open and close sources 
  • Modeling the clinical trial - outcomes, data, statistics

Study design

  • Selecting sites
  • Selecting patients
  • Design protocol
  • Design issues, alerts, kri
  • Modeling outcomes and synthetic data

Study conducting

  • Amending errors on the fly
  • Gathering image data 
  • Gathering non-clear data
  • Catching issues and errors automatically (duplication)
  • Generate outcomes

Finalizing study

  • Recognizing patterns in the data
  • Generating documentation
  • Data validation
  • Predicting outcomes from verified application
  • Research of patterns

All-in-one consultant

LLM consultant

Analyzing open sources

Analyzing private sources

Analyzing images

Generating synthetic data

Predicting outcomes

Validating data

Generating study design

Examples

  • Easier matching of volunteers to the right trials

  • Smarter recruiting using predictions

  • Catching dropouts early with machine learning

  • Making trial changes in real time using AI

  • Finding the best trial sites with data tools

  • Using virtual groups instead of placebos

Problems

  • Private data
  • Regulatory principles
  • Hallucinations
  • Complex problems

Electronic Patient-Reported Outcomes (ePRO)

electronic Clinical Outcome Assessment.

CTMS stands for Clinical Trial Management System.

Electronic Data Capture

AI application

Research

  • Predicting patterns in historical data
  • Hypotheses using articles, data, open and close sources 
  • Modeling the clinical trial - outcomes, data, statistics

Designing study

  • Selecting sites
  • Selecting patients
  • Generating scenarios and protocols
  • Generating alerts and issues

Conducting study

  • Matching patients
  • Error and amendments on the fly
  • Gathering data through non-clear paths
  • Gathering data from images
  • Auto-trusting data
  • Duplication

Finishing study

  • Predicting results for Institution
  • Found broken and successful patterns
  • Generating Synthetic data
  • Analysis competitors

All-in-one AI assistant

  • Collaborating on all stages
  • LLM talking with agents
  • Private data
  • Comfortable interface 
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