Work

Data Fit for Health Equity: Learning Health Systems, AI , and the STANDING Together Recommendations

Year: 2025

Type: article

Abstract: Introduction: Artificial Intelligence (AI) tools may deliver significant improvements in healthcare and Learning Health Systems are well positioned to benefit. However, during the adoption of AI, Learning Health Systems should consider the potential for AI to exacerbate health inequity and perpetuate biases that exist in healthcare and its associated data. Methods: The STANDING Together recommendations provide a method to identify and report potential bias during the curation of datasets for AI and the development of AI from those datasets. The recommendations could form a key learning cycle within a Learning Health System ensuring transparent reporting of healthcare data use and the implementation of AI healthcare technologies that promote health equity. Results and Conclusions: Learning Health Systems are well placed to adopt the STANDING Together best practice recommendations for using healthcare data as they are likely to have both the capabilities to implement the recommendations and the strategic goals that will realize the value of health data and AI that promotes health equity. The STANDING Together recommendations are available from www.datadiversity.org.

Source: Learning Health Systems

DOI: 10.1002/lrh2.70053

Authors: Elinor Laws, Neil Cockburn

Institutions: University of Birmingham, University of Warwick

Language: English

Topic: Artificial Intelligence in Healthcare and Education

Subfield: Health Informatics

Field: Medicine

Domain: Health Sciences

SDGs: Good health and well-being, Reduced inequalities

Open Access status: gold

Estimated APC paid: $2,500