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Artificial Intelligence and Machine Learning to Accelerate Translational Research

Completed

The big data revolution, accompanied by development and deployment of wearable medical devices and mobile health applications, has enabled the biomedical community to access more data with which to apply artificial intelligence and machine learning algorithms. This has created new research opportunities in predictive analytics, precision medicine, virtual diagnosis, patient monitoring, and drug discovery and delivery. This meeting considered the opportunities and challenges related to the application of AI and ML to translational research in biomedicine, and explored how the pharmaceutical and biotechnology industries can learn from adoption and regulation of emerging AI tools and technologies in other sectors.

Description

An ad hoc committee will organize a public workshop to consider how artificial intelligence (AI) and machine learning (ML) can help translate findings in fundamental research into practice and to move therapies into the clinical market. Participants will discuss ethical and regulatory issues related to the application of AI and machine learning to the biomedical field, and explore how the pharmaceutical and biotechnology industries can learn from the opportunities and challenges created by emerging AI technologies in other sectors. The workshop may touch on issues pertaining to workforce preparedness in AI and ML as well. A brief rapporteur-authored workshop summary will be published.

Collaborators

Committee

Jeffrey J. Welser

Chair

C. Taylor Gilliland

Member

Avery Sen

Member

Staff

Susan Sauer Sloan

Lead

Megan Nicholson

Major units and sub-units

National Academy of Sciences

Collaborator

National Academy of Medicine

Collaborator

Policy and Global Affairs

Lead

Government-University-Industry-Philanthropy Research Roundtable

Lead

U.S. Science and Innovation Policy

Lead

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