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Updated: May 24, 2026

An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

A Risk Analysis Tool for Medical Studies.

Alexandre Cotorobai1, Raquel Paradinha1, Jorge M Silva1

  • 1DETI/IEETA, LASI, University of Aveiro, Portugal.

Studies in Health Technology and Informatics
|May 23, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a web-based AI assistant to simplify risk assessment for observational clinical studies. It automates guideline import and reporting, making quality management more accessible for research teams.

Keywords:
clinicalobservationalsqualityregulatory compliancerisk assessment

Related Experiment Videos

Last Updated: May 24, 2026

An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

Area of Science:

  • Clinical research methodology
  • Health informatics
  • Artificial intelligence in healthcare

Background:

  • Clinical research studies encounter significant operational and methodological challenges impacting quality and reproducibility.
  • Traditional risk assessment methods are resource-intensive, requiring specialized expertise and time, posing barriers for research teams.
  • Emerging agentic AI models present opportunities to enhance efficiency in research processes.

Purpose of the Study:

  • To propose an integrated web-based assistant for democratizing risk assessment in observational clinical studies.
  • To address the practical needs of smaller research teams by bridging gaps with commercial quality management systems.
  • To leverage AI for automating key aspects of the risk assessment workflow.

Main Methods:

  • Development of an integrated web-based assistant utilizing agentic AI.
  • Implementation of automated guideline import and structured question-answer workflows.
  • Combination of document-oriented database architecture with workflow orchestration for automated report generation.

Main Results:

  • The proposed tool effectively democratizes risk assessment by automating complex processes.
  • It provides a practical solution for smaller research teams, enhancing quality management in observational studies.
  • The system integrates AI capabilities to streamline guideline adherence and reporting.

Conclusions:

  • The developed web-based assistant offers a novel approach to risk assessment in clinical research.
  • It enhances the quality and reproducibility of observational studies through AI-driven automation.
  • The tool makes advanced risk management more accessible, supporting diverse research team sizes and resource levels.