An equitable and sustainable community of practice framework to address the use of artificial intelligence for global health workforce training

Authors

Seble Frehywot, Department of Global Health & Health Policy and Co-Founder of IT for Health and Education System Equity, George Washington University Milken Institute of Public Health, Washington, DC, USA. seblelf@gwu.edu.
Yianna Vovides, Senior Director of Learning Design and Research at the Center for New Designs in Learning and Scholarship (CNDLS), Curriculum Director for the Learning, Design, and Technology (LDT) program, and Co-Founder of IT for Health and Education System Equity, Georgetown University, Washington, DC, USA.

Document Type

Letter to the Editor

Publication Date

6-13-2023

Journal

Human resources for health

Volume

21

Issue

1

DOI

10.1186/s12960-023-00833-5

Keywords

Artificial Intelligence; Capacity-building; Community of practice; Equity; Machine Learning; Health workforce training

Abstract

Artificial Intelligence (AI) technologies and data science models may hold potential for enabling an understanding of global health inequities and support decision-making related toward possible interventions. However, AI inputs should not perpetuate the biases and structural issues within our global societies that have created various health inequities. We need AI to be able to 'see' the full context of what it is meant to learn. AI trained with biased data produces biased outputs and providing health workforce training with such outputs further contributes to the buildup of biases and structural inequities. The accelerating and intricately evolving technology and digitalization will influence the education and practice of health care workers. Before we invest in utilizing AI in health workforce training globally, it is important to make sure that multiple stakeholders from the global arena are included in the conversation to address the need for training in 'AI and the role of AI in training'. This is a daunting task for any one entity and a multi-sectorial interactions and solutions are needed. We believe that partnerships among various national, regional, and global stakeholders involved directly or indirectly with health workforce training ranging to name a few, from public health & clinical science training institutions, computer science, learning design, data science, technology companies, social scientists, law, and AI ethicists, need to be developed in ways that enable the formation of an equitable and sustainable Communities of Practice (CoP) to address the use of AI for global health workforce training. This paper has laid out a framework for such CoP.

APA Citation

Frehywot, Seble and Vovides, Yianna, "An equitable and sustainable community of practice framework to address the use of artificial intelligence for global health workforce training" (2023). GW Authored Works. Paper 2726.
https://hsrc.himmelfarb.gwu.edu/gwhpubs/2726

Department

Global Health