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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.