Diversity and inclusion within datasets in heart failure: a systematic review
Laws, Elinor Charalambides, Maria Vadera, Sonam Keller, Eva Alderman, Joseph Blackboro, Breanna Hogg, Jeffry Salisbury, Thomas Palmer, Joanne Calvert, Melanie
Laws, Elinor
Charalambides, Maria
Vadera, Sonam
Keller, Eva
Alderman, Joseph
Blackboro, Breanna
Hogg, Jeffry
Salisbury, Thomas
Palmer, Joanne
Calvert, Melanie
Affiliation
University Hospitals Birmingham NHS Foundation Trust; University of Birmingham; University of Southampton; University Hospitals Leicester NHS Trust; University College London; South Tyneside and Sunderland NHS Foundation Trust; University of Oxford Hospitals NHS Foundation Trust; The Hospital for Sick Children; Independent Cancer Patients' Voice; University of Sheffield
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Publication date
2025-03-26
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Abstract
Background: Heart failure (HF) is a life-threatening disease affecting 64 million people worldwide. Artificial intelligence (AI) technologies are being developed for use in HF to support early diagnosis and stratification of treatment. The performance characteristics of AI technologies are influenced by whether the data used during the AI lifecycle reflects the populations for which the AI is used.
Objectives: The aim of the study was to identify and characterize datasets used across the lifecycle of AI technologies for HF, focusing on data diversity and inclusivity.
Methods: MEDLINE and Embase were systematically searched from January 1, 2012, until August 30, 2022, to identify articles relating to the development of AI in HF. Articles were independently screened by 2 reviewers to identify datasets. Dataset documentation was analyzed with a focus on accessibility, geographical origin, relevant metadata reporting, and dataset composition.
Results: The 72 datasets identified represented 23 countries and over 2 million individuals. In total, 62 (86%) datasets reported "age," 61 (85%) reported sex or gender, 21 (29%) reported race and/or ethnicity, and 8 (11%) reported socioeconomic status. In the 21 datasets that reported race and/or ethnicity, 89% of individuals represented were reported within the "White" or "Caucasian" category. Only 20 (28%) datasets were fully accessible.
Conclusions: Reporting of sex, gender, and socioeconomic status in HF datasets is inconsistent. There is a need to generate datasets that are transparently reported and accessible. Although collecting and reporting demographic attributes is complex and needs to be undertaken with appropriate safeguards, it is also an essential step toward building equitable AI-based health technologies.
Citation
Laws E, Charalambides M, Vadera S, Keller E, Alderman J, Blackboro B, Hogg J, Salisbury T, Palmer J, Calvert M, Mackintosh M, Matin R, Sapey E, Ordish J, McCradden M, Mateen B, Gath J, Adebajo A, Kuku S, Bradlow W, Denniston AK, Liu X. Diversity and Inclusion Within Datasets in Heart Failure: A Systematic Review. JACC Adv. 2025 Mar;4(3):101610. doi: 10.1016/j.jacadv.2025.101610.
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Article