Can AI Improve Global Health Without Trust, Equity, and Accountability?
David Lazerson, Co-Founder and CEO of Briya, and Jonathan Samet, Professor in the Department of Environmental & Occupational Health and the Department of Epidemiology at the Colorado School of Public Health, join host Christina Raish of FINN Partners for a People and Planet United Global Health and Purpose Summit session on the future of AI in global health.
AI is already moving through healthcare, research, public health, and regulatory environments at speed. The question is no longer whether AI will be used, but how it will be governed, validated, adapted, and held accountable across different healthcare settings and populations. Lazerson frames the discussion around the reality that AI is already reshaping healthcare research and clinical practice, while Samet brings a public health perspective to the responsibilities and risks that come with global deployment.
The discussion connects technology, accountability, equity, and public health purpose. AI tools can help interpret diagnostic materials, improve clinical records, support research workflows, strengthen disease burden analysis, extend expertise into lower-resource settings, and democratize research and development. Those opportunities depend on representative data, local relevance, quality control, implementation capacity, and clear responsibility across the full path from development to use.
Global health requires more than technical capability. A tool developed in one healthcare environment may not work safely or effectively in another unless it is adapted to local infrastructure, disease patterns, training capacity, records, and clinical realities. The value of AI depends on whether health systems, researchers, technology developers, regulators, funders, and public health institutions can create the conditions for trust and accountability.
AI can improve global health when its use is guided by trust, equity, accountability, representative data, quality control, and local relevance. Its future value will depend not only on what the technology can do, but on the responsibility of the systems and leaders who decide how it is used.
Session Intelligence
AI in global health is moving from possibility to implementation. The central challenge is how leaders ensure that adoption advances with trust, equity, and accountability.
Leadership Focus
AI can expand healthcare and public health capacity, but its value depends on responsible governance, local adaptation, and fit-for-purpose validation.
Strategic Relevance
Diagnostics, clinical records, public health surveillance, disease burden analysis, research workflows, and global health delivery can all benefit from accountable AI.
Execution Model
Organizations need clear responsibility across the full chain from development to deployment, including data quality, representative training data, local adaptation, user training, and impact evaluation.
Core Takeaway
AI will earn trust in global health when it helps improve care and public health outcomes while reducing inequity, strengthening accountability, and protecting the people and populations it is meant to serve.
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