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Minimizing Bias and Maximizing Long-Term Accuracy, Utility and Generalizability of Predictive Algorithms in Health Care Challenge is sponsored by National Center for Advancing Translational Sciences (NCATS) - NIH. This challenge seeks to encourage the development of bias-detection and -correction tools that foster “good algorithmic practice” and mitigate the risk of unwitting bias in clinical decision support algorithms.
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Or search similar grants →According to the current listing, eligibility includes: Participants across academia and the private sector, in teams, as representatives of an academic or private entity, or in an individual capacity. Confirm the full requirements in the official notice before applying.
Minimizing Bias and Maximizing Long-Term Accuracy, Utility and Generalizability of Predictive Algorithms in Health Care Challenge is funded by National Center for Advancing Translational Sciences (NCATS) - NIH. Verify program details on the funder's official page before applying.
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Advance from NIH SBIR Phase I to Phase II with tips on demonstrating feasibility, building a commercialization plan, and securing $1M in funding.
Read articlePAR-27-056 replaces the long-running IDeA-CTR U54 cooperative agreement with a P30 center core grant and splits the program into Development and Network tracks. Posted August 19, 2026, with a first estimated due date of October 9. Here is what the mechanism swap actually changes for the 23 IDeA states and Puerto Rico.
Read articleThe U.S. Government Policy for Stopping High-Risk Life Sciences Research, released July 28, 2026 under EO 14292 and implemented at NIH through NOT-OD-26-101, replaces mitigation with prohibition. It bans dangerous gain-of-function research outright, gates potential DGOF behind government-wide review, and creates International Research of Concern — a research-security regime wearing biosafety clothing. Penalties run to five-year debarment and False Claims Act exposure.
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