An equitable and sustainable community of practice framework to address the use of artificial intelligence for global 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...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
BMC
2023-06-01
|
| Series: | Human Resources for Health |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12960-023-00833-5 |
| _version_ | 1797801384724135936 |
|---|---|
| author | Seble Frehywot Yianna Vovides |
| author_facet | Seble Frehywot Yianna Vovides |
| author_sort | Seble Frehywot |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-13T04:49:39Z |
| format | Article |
| id | doaj.art-94201d9288614601a9a827385adbdc7c |
| institution | Directory Open Access Journal |
| issn | 1478-4491 |
| language | English |
| last_indexed | 2024-03-13T04:49:39Z |
| publishDate | 2023-06-01 |
| publisher | BMC |
| record_format | Article |
| series | Human Resources for Health |
| spelling | doaj.art-94201d9288614601a9a827385adbdc7c2023-06-18T11:16:19ZengBMCHuman Resources for Health1478-44912023-06-012111710.1186/s12960-023-00833-5An equitable and sustainable community of practice framework to address the use of artificial intelligence for global health workforce trainingSeble Frehywot0Yianna Vovides1Department of Global Health & Health Policy and Co-Founder of IT for Health and Education System Equity, George Washington University Milken Institute of Public HealthSenior 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 UniversityAbstract 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.https://doi.org/10.1186/s12960-023-00833-5Artificial IntelligenceCommunity of practiceHealth workforce trainingEquity; Machine LearningCapacity-building |
| spellingShingle | Seble Frehywot Yianna Vovides An equitable and sustainable community of practice framework to address the use of artificial intelligence for global health workforce training Human Resources for Health Artificial Intelligence Community of practice Health workforce training Equity; Machine Learning Capacity-building |
| title | An equitable and sustainable community of practice framework to address the use of artificial intelligence for global health workforce training |
| title_full | An equitable and sustainable community of practice framework to address the use of artificial intelligence for global health workforce training |
| title_fullStr | An equitable and sustainable community of practice framework to address the use of artificial intelligence for global health workforce training |
| title_full_unstemmed | An equitable and sustainable community of practice framework to address the use of artificial intelligence for global health workforce training |
| title_short | An equitable and sustainable community of practice framework to address the use of artificial intelligence for global health workforce training |
| title_sort | equitable and sustainable community of practice framework to address the use of artificial intelligence for global health workforce training |
| topic | Artificial Intelligence Community of practice Health workforce training Equity; Machine Learning Capacity-building |
| url | https://doi.org/10.1186/s12960-023-00833-5 |
| work_keys_str_mv | AT seblefrehywot anequitableandsustainablecommunityofpracticeframeworktoaddresstheuseofartificialintelligenceforglobalhealthworkforcetraining AT yiannavovides anequitableandsustainablecommunityofpracticeframeworktoaddresstheuseofartificialintelligenceforglobalhealthworkforcetraining AT seblefrehywot equitableandsustainablecommunityofpracticeframeworktoaddresstheuseofartificialintelligenceforglobalhealthworkforcetraining AT yiannavovides equitableandsustainablecommunityofpracticeframeworktoaddresstheuseofartificialintelligenceforglobalhealthworkforcetraining |