The value of standards for health datasets in artificial intelligence-based applications

Artificial intelligence as a medical device is increasingly being applied to healthcare for diagnosis, risk stratification and resource allocation. However, a growing body of evidence has highlighted the risk of algorithmic bias, which may perpetuate existing health inequity. This problem arises in part because of systemic inequalities in dataset curation, unequal opportunity to participate in research and inequalities of access. This study aims to explore existing standards, frameworks and best practices for ensuring adequate data diversity in health datasets. Exploring the body of existing literature and expert views is an important step towards the development of consensus-based guidelines. The study…

• MI
  Massachusetts Institute of Technology
• WT
  Wellcome Trust
• NP
  Nuclear Power Institute of China

  Award: 104687
• UH
  University Hospitals Birmingham NHS Foundation Trust
• GO
  Government of the United Kingdom
• UR
  UK Research and Innovation

  Award: 104687
• NI
  National Institute for Health and Care Research

  Award: AI_HI200014
• UC
  University College London
• UO
  University of Oxford
• UO
  University of Leeds
• EC
  European Commission
• LU
  Linköpings Universitet
• HF
  Hospital for Sick Children
• OU
  Oxford University Hospitals NHS Foundation Trust
• ME
  Moorfields Eye Hospital NHS Foundation Trust
• MR
  Medical Research Council

  Awards: MC_PC_21055, MC_PC_21015
• GO
  Great Ormond Street Institute of Child Health
• BB
  Birmingham Biomedical Research Centre
• NI
  National Institute on Minority Health and Health Disparities