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Data Fit for Health Equity: Learning Health Systems, AI, and the STANDING Together Recommendations
Elinor Laws1, Neil Cockburn1,2
1Department of Applied Health Sciences University of Birmingham Birmingham UK.
Learning Health Systems
|February 16, 2026
Summary
Artificial Intelligence (AI) can improve healthcare, but Learning Health Systems must address potential biases. The STANDING Together recommendations offer a method to ensure AI promotes health equity by identifying and reporting data biases.
Area of Science:
- Healthcare innovation
- Health equity
- Artificial Intelligence in Medicine
Background:
- Learning Health Systems (LHS) are poised to leverage Artificial Intelligence (AI) for significant healthcare advancements.
- However, the integration of AI in healthcare carries the risk of amplifying existing health inequities and data biases.
Purpose of the Study:
- To introduce the STANDING Together recommendations as a framework for addressing bias in AI development within LHS.
- To highlight how these recommendations can foster transparent data use and promote health equity.
Main Methods:
- The STANDING Together recommendations provide a systematic approach to identify and report biases during AI dataset curation and model development.
- These recommendations are designed to integrate into the learning cycles of LHS.
Main Results:
- LHS possess the infrastructure and strategic alignment to implement the STANDING Together recommendations effectively.
- Adoption of these best practices can lead to the development and deployment of AI that actively promotes health equity.
Conclusions:
- The STANDING Together recommendations are crucial for LHS aiming to harness AI's potential while mitigating risks of bias and inequity.
- Implementing these guidelines ensures responsible AI adoption, aligning with the goals of promoting health equity in healthcare.
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