The National Center for Advancing Translational Sciences (NCATS) team within the NIH is launching the Minimizing Bias and Maximizing Long-term Accuracy, Utility, and Generalizability of Predictive Algorithms in Healthcare Challenge. Although AI/ML algorithms offer promise for clinical decision making, that potential has yet to be fully realized in healthcare. Even well-designed AI/ML algorithms and models can become inaccurate or unreliable over time due to various factors; changes in data distribution, subtle shifts in the data, real world interactions, user behavior, and shifts in data capture and management practices can have repercussions for model performance. These subtle shifts over time can cause degradation of the predictive capability of an algorithm, which can effectively negate the benefits of these types of systems in the clinic. Accurate monitoring of an algorithm’s behavior and the ability to flag material drifts in performance may enable timely adjustments that ensure the model’s predictions remain accurate, fair, and unbiased over time. In this way, degradation of the predictive capability of the algorithm when applied in the real world may be prevented.
Partner organization: The National Institutes of Health
Award: $700,000 in total prizes
Open Date: October 31, 2022
Close Date: March 31, 2023
For more information, visit: https://expeditionhacks.com/bias-detection-healthcare/