The SPARK PubMed/PMC Challenge from NIH Challenges seeks AI systems that generate citation-backed, synthesized answers to complex biomedical queries using PubMed and PMC data.
NIH Challenges' SPARK dbGaP Challenge funds AI tools to semantically normalize dbGaP data and create conversational interfaces for improved genomic and phenotypic research access.
NIH Challenges' Grand Challenge seeks innovative, AI-ready data integration and analytics solutions to advance whole-person chronic disease disparities research.
NIH Challenges' Real-World Evidence Innovation Challenge seeks to benchmark and validate analytic methods for clinical data, emphasizing reproducibility and robustness across diverse health systems.
NIH Challenges funds digital twin and synthetic data modeling research to advance whole-person health and reduce chronic disease disparities through integrated, AI-ready computational platforms.