A blueprint for equitable, ethical AI research

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Artificial intelligence (AI) has huge potential to advance the field of health and medicine, but the nation must be prepared to responsibly harness the power of AI and maximize its benefits, according to an editorial by Victor J. Dzau and colleagues.

In addition to addressing key issues of equity across the innovation lifecycle, the authors argue that the scientific community must also decrease barriers to entry for large-scale AI capabilities and create dynamic, collaborative ecosystems for research and governance.

The authors include suggestions for how the scientific community can tackle these challenges: First, advancing AI infrastructure for data, computation, health, and scale, in order to democratize access to both research and outcomes.

Second, creating a flexible governance framework to ensure equity, prevent unintended consequences, and maximize positive impact.

Third, building international collaborative efforts to efficiently expand scope and scale and to effectively address research questions of key interest to the global community.

The National Academies are capable of playing a key role by convening stakeholders, enabling cross-sectoral discussions, and providing evidence-based recommendations in these areas, according to the authors. To see the ultimate vision of AI in health and medicine realized, the authors conclude, the scientific community must expand current capacity-building and governance efforts to successfully construct a strong foundation for the future.

In the same issue, Monica Bertagnolli, incoming director of the National Institutes of Health shares her perspective on the same topic.

The article is published in the journal PNAS Nexus.

More information:
Melissa Laitner et al, Achieving the promise of artificial intelligence in health and medicine: Building a foundation for the future, PNAS Nexus (2023). DOI: 10.1093/pnasnexus/pgad410. academic.oup.com/pnasnexus/art … 2/12/pgad410/7477225

Victor J Dzau et al, Advancing health through artificial intelligence/machine learning: The critical importance of multidisciplinary collaboration, PNAS Nexus (2023). DOI: 10.1093/pnasnexus/pgad356. academic.oup.com/pnasnexus/art … 2/12/pgad356/7477226

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PNAS Nexus

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A blueprint for equitable, ethical AI research (2023, December 19)
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