Health Equity and Ethical Considerations in Using Artificial Intelligence in Public Health and Medicine

PEER REVIEWED

This figure illustrates the multifaceted approach required for ethical and equitable AI implementation in public health and medicine. It emphasizes the interconnected nature of data practices, ethical frameworks, community engagement, education, and continuous evaluation. The image consists of a large circle at the center of which is a circle labeled “AI in Public Health and Medicine” surrounded by 5 smaller circles. One circle, labeled “Inclusive data practices,” has 3 bullet points: Diverse data collection, Data equity audits, and Accessible AI solutions. A second circle, labeled “Ethical frameworks,” has 3 bullet points: Ethical standards, Regulatory compliance, and Transparent algorithms. A third circle, labeled “Community engagement,” has 3 bullet points: Working partner inclusion, Cultural competence, and Feedback mechanisms. A fourth circle, labeled “Public and professional education,” has 3 bullet points: Continuous training, Public awareness, and Patient-centered design. A fifth circle, labeled “Monitoring and evaluation” has 3 bullet points: Continuous monitoring, Risk management, and Interdisciplinary collaboration. To the left of the large main circle are 4 text boxes with arrows pointing toward the circle. From the top, these text boxes are labeled “Bias mitigation,” “Diverse funding,” “Ethical data use,” and “Interdisciplinary teams.”

Figure.
Multifaceted approach for ethical and equitable implementation of artificial intelligence (AI) in public health and medicine.

Return to Article

Top