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AI in Biomedicine-A Forward-Looking Perspective on Health Equity.

Deepak Kumar1, Bradley A Malin2, Jamboor K Vishwanatha3

  • 1The Julius L. Chambers Biomedical/Biotechnology Research Institute (JLC-BBRI), Department of Pharmaceutical Sciences, North Carolina Central University (NCCU), Durham, NC 27707, USA.

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Summary

Artificial intelligence (AI) can advance health equity by improving patient outcomes, especially for underserved communities. Addressing algorithmic bias and ensuring diverse datasets are crucial for responsible AI in healthcare.

Keywords:
RCMIartificial intelligenceaugmented intelligencehealth disparitieshealth equityhealth ethicsmachine learning

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Area of Science:

  • Health Equity
  • Artificial Intelligence in Healthcare
  • Biomedical Informatics

Background:

  • Artificial intelligence (AI) is rapidly transforming healthcare, presenting opportunities to advance health equity.
  • The 2024 Research Centers in Minority Institutions (RCMI) Consortium National Conference addressed AI's role, challenges, and potential in promoting health equity.
  • Key challenges include algorithmic bias, lack of transparency, and under-representation of minority groups in AI datasets.

Purpose of the Study:

  • To summarize discussions on AI's potential and challenges in advancing health equity.
  • To highlight the role of RCMI institutions in AI research and development.
  • To emphasize the need for ethical governance and inclusive practices in AI deployment.

Main Methods:

  • Summary of presentations and discussions from the "Artificial Intelligence: Safely, Ethically, and Responsibly" conference session.
  • Focus on expert insights regarding AI's capabilities, ethical considerations, and national initiatives (e.g., AIM-AHEAD).
  • Exploration of RCMI institutions' contributions to diverse AI research and culturally competent tool development.

Main Results:

  • AI offers significant potential to improve patient outcomes, particularly for underserved populations.
  • Algorithmic bias, data under-representation, and transparency issues are critical barriers to equitable AI.
  • RCMI institutions are vital for fostering diverse AI/machine learning research and developing inclusive AI tools.

Conclusions:

  • Ensuring health equity in AI requires robust ethical standards, a diverse scientific workforce, and inclusive data practices.
  • RCMI institutions play a critical role in guiding AI development and deployment towards equitable healthcare.
  • Prioritizing health equity in AI is essential for creating an inclusive AI-driven healthcare system.