Revealing transparency gaps in publicly available COVID-19 datasets used for medical artificial intelligence development-a systematic review
Abstract
During the COVID-19 pandemic, artificial intelligence (AI) models were created to address health-care resource constraints. Previous research shows that health-care datasets often have limitations, leading to biased AI technologies. This systematic review assessed datasets used for AI development during the pandemic, identifying several deficiencies. Datasets were identified by screening articles from MEDLINE and using Google Dataset Search. 192 datasets were analysed for metadata completeness, composition, data accessibility, and ethical considerations. Findings revealed substantial gaps: only 48% of datasets documented individuals' country of origin, 43% reported age, and under 25% included sex, gender, race, or ethnicity. Information on data labelling, ethical review, or consent was frequently missing. Many datasets reused data with inadequate traceability. Notably, historical paediatric chest x-rays appeared in some datasets without acknowledgment. These deficiencies highlight the need for better data quality and transparent documentation to lessen the risk that biased AI models are developed in future health emergencies.
Author
Alderman, Joseph E
Charalambides, Maria
Sachdeva, Gagandeep
Laws, Elinor
Palmer, Joanne
Lee, Elsa
Menon, Vaishnavi
Malik, Qasim
Vadera, Sonam
Calvert, Melanie
Ghassemi, Marzyeh
McCradden, Melissa D
Ordish, Johan
Mateen, Bilal
Summers, Charlotte
Gath, Jacqui
Matin, Rubeta N
Denniston, Alastair K
Liu, Xiaoxuan
Charalambides, Maria
Sachdeva, Gagandeep
Laws, Elinor
Palmer, Joanne
Lee, Elsa
Menon, Vaishnavi
Malik, Qasim
Vadera, Sonam
Calvert, Melanie
Ghassemi, Marzyeh
McCradden, Melissa D
Ordish, Johan
Mateen, Bilal
Summers, Charlotte
Gath, Jacqui
Matin, Rubeta N
Denniston, Alastair K
Liu, Xiaoxuan
Citations
Altmetric:
Date
2024-10-23
Type
Article
Subject
Patients. Primary care. Medical profession. Forensic medicine, Public health. Health statistics. Occupational health. Health education, Health services. Management
Collections
Citation
Alderman JE, Charalambides M, Sachdeva G, Laws E, Palmer J, Lee E, Menon V, Malik Q, Vadera S, Calvert M, Ghassemi M, McCradden MD, Ordish J, Mateen B, Summers C, Gath J, Matin RN, Denniston AK, Liu X. Revealing transparency gaps in publicly available COVID-19 datasets used for medical artificial intelligence development-a systematic review. Lancet Digit Health. 2024 Nov;6(11):e827-e847. doi: 10.1016/S2589-7500(24)00146-8.
Journal / Source Title
The Lancet Digital Health
DOI
10.1016/S2589-7500(24)00146-8
PMID
39455195
Publisher
Elsevier
Publisher’s URL
https://www.thelancet.com/journals/landig/home