Building competency in artificial intelligence and bias mitigation for nurse scientists and aligned health researchers.

Healthcare systems are increasingly integrating artificial intelligence and machine learning (AI/ML) tools into patient care, potentially influencing clinical decisions for millions. However, concerns are growing about these tools reinforcing systemic inequities. To address bias in AI/ML tools and promote equitable outcomes, guidelines for mitigating this bias and comprehensive workforce training programs are necessary. In response, we developed the multifaceted Human-Centered Use of Multidisciplinary AI for Next-Gen Education and Research (HUMAINE), informed by a comprehensive scoping review, training workshops, and a research symposium. The curriculum, which focuses on structural inequities in algorithms that contribute to health disparities, is designed to equip scientists with AI/ML competencies that allow them to effectively address these structural inequities and promote health equity. The curriculum incorporates the perspectives of clinicians, biostatisticians, engineers, and policymakers to harness AI's transformative potential, with the goal of building an inclusive ecosystem where cutting-edge technology and ethical AI governance converge to create a more equitable healthcare future for all.

Duke Scholars

Author Michael Paul Cary School of Nursing

Author Jacquelyn McMillian-Bohler School of Nursing

Author Benjamin Alan Goldstein Biostatistics & Bioinformatics, Division of Translational Bi ...

Author Susan Gray Silva School of Nursing