Request for Information (RFI): Input to Advance Artificial Intelligence (AI)-ready Data Generation and Scalable Computational Approaches in NIAMS Research

Funding Agency:
National Institutes of Health

Through this Request for Information (RFI), the National Institutes of Arthritis and Musculoskeletal and Skin Diseases (NIAMS) invites public comments on the needs and opportunities for developing, advancing, and implementing scalable computational approaches; and for generating new or re-purposing existing artificial intelligence (AI)-ready datasets in support of research across all NIAMS portfolios.

Rapid increase in the volume of biomedical data being collected brings both exciting opportunities and significant challenges. The complex nature of the underlying problems and of the data itself make modeling and computational analysis indispensable for generating hypotheses, interpreting results, and developing precision treatments. Proper annotation and metadata standards are essential to ensure that data are findable and reusable, as well as compatible with AI-based machine learning algorithms. Storage and analysis must be scalable and should ideally take advantage of cloud-based repositories. Adherence to the “FAIR” (Findable, Accessible, Interoperable, Reusable) data principles calls for well-developed indexing, sharing, and sustainability approaches. Tool development and computational analysis require collaboration between quantitative, biological, and clinical scientists. Through this RFI, NIAMS will identify challenges, opportunities, and specific needs pertaining to computational and modeling approaches (including large-scale AI and machine learning methods), as well as those related to the generation of new AI-ready datasets or re-purposing of existing datasets in musculoskeletal, rheumatic, and skin diseases research.

NIAMS invites input from researchers in academia or industry, healthcare professionals, patient advocates and health advocacy organizations, scientific or professional organizations, Federal agencies, and other interested members of the public to help define community needs pertaining to computational and modeling approaches (including large-scale AI and machine learning methods) and their scalability, as well as those related to creating new datasets or re-purposing existing datasets in order to advance NIAMS-relevant research.

Scientific and professional organizations are strongly encouraged to submit a single response that reflects the views of their organization and membership as a whole.

Response Date: Sep. 1, 2020

Eligibility

Faculty

Category

Engineering and Physical Sciences
Medical
Medical - Basic Science
Medical - Clinical Science
Medical - Translational

External Deadline

September 1, 2020