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Summary

Generative artificial intelligence (GenAI) offers transformative potential in public health, but requires a three-dimensional governance framework balancing technical, institutional, and ethical considerations for equitable outcomes.

Keywords:
algorithmic fairnessdata colonialismethical machine learningexplainable AIgenerative artificial intelligencehealth equitymedical AI governancepublic health informatics

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Area of Science:

  • Public Health Informatics
  • Artificial Intelligence in Medicine
  • Health Governance

Background:

  • Generative artificial intelligence (GenAI) is increasingly applied in public health for disease surveillance, resource allocation, and clinical decision-making.
  • Current efficiency-driven interventions highlight systemic conflicts between algorithmic fairness, rapid innovation, and regulatory gaps.
  • Cross-border data flows challenge local ethical values, necessitating new governance approaches.

Purpose of the Study:

  • To propose a three-dimensional governance structure for GenAI in public health and medicine.
  • To address the inherent contradictions between technical efficiency and ethical considerations.
  • To guide the responsible development and deployment of AI in healthcare.

Main Methods:

  • A three-dimensional governance framework encompassing technical, institutional, and ethical domains.
  • Exploration of technology-focused solutions like explainability and culturally-aware design.
  • Examination of institutional strategies including privacy-preserving platforms and risk-based regulation.
  • Consideration of ethical principles such as incorporating local values and equitable AI dividend distribution.

Main Results:

  • Technical solutions can enhance transparency and cultural sensibility.
  • Institutional frameworks can balance innovation with accountability through privacy and regulation.
  • Ethical considerations promote equitable health outcomes by integrating local values.
  • Persistent challenges include algorithmic bias, data imperialism, and opacity in medical AI.

Conclusions:

  • A balanced approach integrating technical, institutional, and ethical dimensions is crucial for trusted AI ecosystems in healthcare.
  • Future priorities include developing comprehensive measurement tools, transnational governance, and participatory design.
  • Achieving human-centered healthcare with AI necessitates fairness, accessibility, social responsiveness, and justice.