{"id":11684,"date":"2020-06-01T18:34:47","date_gmt":"2020-06-01T22:34:47","guid":{"rendered":"https:\/\/research.ncsu.edu\/rdo\/?p=11684"},"modified":"2022-09-06T13:17:55","modified_gmt":"2022-09-06T17:17:55","slug":"spotlight-on-nihs-interests-in-artificial-intelligence","status":"publish","type":"post","link":"https:\/\/research.ncsu.edu\/rdo\/spotlight-on-nihs-interests-in-artificial-intelligence\/","title":{"rendered":"Spotlight on NIH\u2019s Interests in Artificial Intelligence"},"content":{"rendered":"\n

We are nearly halfway through the NIH Institutes\u2019 spring Advisory Council sessions and one question has come up again and again: What is NIH\u2019s vision for Data, Artificial Intelligence, and Machine Learning?<\/span><\/p>\n\n\n\n

This question is also coming up on NC State\u2019s campus. With the COVID-19 pandemic on everyone\u2019s mind, faculty from the computational side of campus are taking a new look at potential biomedical applications, while faculty on the health side of campus are taking a new look at how to better leverage their data to respond to emergencies like what we are currently facing.<\/span><\/p>\n\n\n\n

The answer is that NIH\u2019s vision is still very much a work-in-progress, but some major cues have come from the NIH Advisory Committee to the Director (ACD) <\/span>Working Group on Artificial Intelligence\u2019s Final Report<\/span><\/a>.  This report-out, published this past December, highlighted several recommendations that NIH is now looking at potentially turning into Funding Opportunities or other types of activities. <\/span><\/p>\n\n\n\n

ACD Working Group on AI\u2019s Recommendations:<\/b><\/p>\n\n\n\n

  1. Support flagship data generation efforts to propel progress by the scientific community.<\/span><\/li>
  2. Develop and publish criteria for ML-friendly datasets.<\/span><\/li>
  3. Design and apply \u201cdatasheets\u201d and \u201cmodel cards\u201d for biomedical ML.<\/span><\/li>
  4. Develop and publish consent and data access standards for biomedical ML.<\/span><\/li>
  5. Publish ethical principles for the use of ML in biomedicine.<\/span><\/li>
  6. Develop curricula to attract and train ML-BioMed experts.<\/span><\/li>
  7. Expand the pilot for ML-focused trainees and fellows.<\/span><\/li>
  8. Convene cross-disciplinary collaborators.<\/span><\/li><\/ol>\n\n\n\n

    While the NIH Institutes and Centers are pondering which recommendations they will advance and how, one of the early initiatives appears to be an effort coming from the NIH Common Fund (and led by the National Library of Medicine, National Human Genome Research Institute, and National Institute of Biomedical Imaging and Bioengineering) to develop Centers specifically addressing Recommendation #2. This initiative for the <\/span>Artificial Intelligence for Biomedical Excellence<\/span><\/a> was presented at the May 15<\/span>th<\/span> meeting of the NIH Council of Councils. Stay tuned to the NC State Funding Listserv, for coming funding opportunity announcements.<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"

    We are nearly halfway through the NIH Institutes\u2019 spring Advisory Council sessions and one question has come up again and again: What is NIH\u2019s vision for Data, Artificial Intelligence, and…<\/p>\n","protected":false},"author":926,"featured_media":14948,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"ncst_custom_author":"","ncst_show_custom_author":false,"ncst_dynamicHeaderBlockName":"ncst\/default-post-header","ncst_dynamicHeaderData":"{\"showAuthor\":true,\"showDate\":true,\"showFeaturedVideo\":false,\"caption\":\"Dr. Alper Bozkurt does his research on Centennial Campus in EBII. 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