The US National Institutes of Health (NIH) put together an AI working group to evaluate what needs to be accomplished to leverage the exponential opportunities AI can provide to biomedical research. In an article published by governmentCIO journal the group recommended the following eight recommendations:
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Support flagship data generation efforts to propel progress by the scientific community.
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Develop and publish criteria for machine learning-friendly datasets.
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Design and apply “datasheets” and “model cars” for biomedical machine learning.
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Develop and publish consent and data access standards for biomedical machine learning.
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Publish ethical principles for the use of machine learning in biomedicine.
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Develop curricula to attract and train machine learning-biomedicine experts.
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Expand the pilot for machine learning-focused trainees and fellows.
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Convene cross-disciplinary collaborators.
In addition to these recommendations Group Co-Chair David Glazier, eloquently articulates that “Two revolutions shaping how the world thinks about technology: generating data and analyzing it. This encompasses biomedical data generated specifically for machine learning and also machine learning that is designed for biomedical experiments, which need harmonization of data, people and ethics.”