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An intriguing vision for transatlantic collaborative health data use and artificial intelligence development.

Daniel C Baumgart1

  • 1Precision Health Signature Area, College of Health Sciences, College of Natural and Applied Sciences all at University of Alberta, Edmonton, Alberta, Canada. baumgart@ualberta.ca.

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Summary

Precision health requires integrated systems and artificial intelligence (AI) for better patient outcomes. Developing dependable AI necessitates collaborative, diverse data sets and a cultural shift towards trust in precompetitive data sharing.

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Area of Science:

  • Digital Health
  • Health Informatics
  • Artificial Intelligence in Medicine

Background:

  • Traditional healthcare struggles with vast data volumes, hindering therapeutic success and innovation.
  • Precision health, delivering tailored treatments, relies on integrated learning health systems.
  • Artificial intelligence (AI) shows promise in risk stratification, diagnosis, and reducing health disparities.

Purpose of the Study:

  • To highlight the limitations of current healthcare data processing.
  • To advocate for precision health through learning health systems and AI.
  • To emphasize the need for collaborative, representative data for AI development.

Main Methods:

  • Discusses the requirements for dependable AI models, including large, population-representative datasets.
  • Highlights the necessity of multidisciplinary, multinational teams to mitigate bias.
  • References the Data for Health (#DFH23) conference and Harvard workshop as examples of stakeholder engagement.

Main Results:

  • AI models require diverse, large-scale data and collaborative efforts to avoid bias.
  • Current data infrastructure is insufficient for developing and validating AI at national or institutional levels.
  • A new dimension of collaboration and trust in precompetitive data sharing is essential.

Conclusions:

  • The European Health Data Space (EHDS) and G7 Hiroshima AI process provide frameworks for data collaboration.
  • A cultural shift towards trust and precompetitive data sharing is crucial for advancing AI in medicine.
  • Full support for digital transformation in medicine, research, and innovation, including AI, is called for.