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  1. Home
  3. Research Domains
  5. Information And Computing Sciences
  7. Data Management And Data Science
  9. Query Processing And Optimisation
  11. Biomedical Data Repositories Require Governance For Artificial Intelligence/machine Learning Applications At Every Step.
  1. Home
  3. Research Domains
  5. Information And Computing Sciences
  7. Data Management And Data Science
  9. Query Processing And Optimisation
  11. Biomedical Data Repositories Require Governance For Artificial Intelligence/machine Learning Applications At Every Step.

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Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System
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Biomedical data repositories require governance for artificial intelligence/machine learning applications at every step.

Ellen Wright Clayton1, Susannah Rose2, Camille Nebecker3

  • 1Center for Biomedical Ethics and Society, Vanderbilt University Medical Center, Nashville, TN 37203, United States.

JAMIA Open
|December 3, 2025

View abstract on PubMed

Summary
This summary is machine-generated.

The NIH's Bridge2AI Program created AI-ready biomedical datasets, facing challenges in data collection and governance. Lessons learned emphasize responsible data use and ethical considerations for future AI initiatives.

Keywords:
data accessdata privacygovernanceinformed consent

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Area of Science:

  • Biomedical data science
  • Artificial intelligence in healthcare
  • Data governance

Background:

  • The National Institutes of Health (NIH) launched the Bridge2AI Program to foster AI adoption in biomedical research.
  • Four flagship datasets were funded, designed for use with artificial intelligence (AI) and machine learning (ML) technologies.
  • Ensuring responsible data collection and governance is crucial for the ethical application of AI in health.

Purpose of the Study:

  • To discuss the challenges and lessons learned in collecting and governing AI-ready biomedical datasets.
  • To outline the steps and ethical considerations in creating and utilizing these datasets.
  • To highlight strategies for responsible data management in AI-driven research.

Main Methods:

  • Data selection criteria and justification.
  • Incorporation of public concerns and participant consent.
  • Establishing protocols for data storage, access, sharing, and downloading.
  • Addressing ethical, legal, social, and practical challenges throughout the data lifecycle.

    • Main Results:

    • Identified ethical, legal, social, and practical challenges in AI-ready dataset creation.
    • Highlighted diverse project-specific choices in public input, data storage, and access control.
    • Demonstrated the importance of addressing future data storage and use concerns.

    Conclusions:

    • Bridge2AI dataset governance processes, though varied, share common elements.
    • These common elements offer valuable strategies for future AI-focused data programs.
    • Lessons learned underscore the need for robust data governance frameworks to ensure responsible AI implementation in biomedical research.