Revealing transparency gaps in publicly available COVID-19 datasets used for medical artificial intelligence development-a systematic review

Joseph E Alderman¹, Maria Charalambides², Gagandeep Sachdeva³, Elinor Laws¹, Joanne Palmer¹, Elsa Lee⁴, Vaishnavi Menon⁵, Qasim Malik⁶, Sonam Vadera⁷, Melanie Calvert⁸, Marzyeh Ghassemi⁹, Melissa D McCradden¹⁰, Johan Ordish¹¹, Bilal Mateen¹², Charlotte Summers¹³, Jacqui Gath¹⁴, Rubeta N Matin¹⁵, Alastair K Denniston¹⁶, Xiaoxuan Liu¹⁷

Affiliations

¹ Queen Elizabeth Hospital, University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK; Institute of Inflammation and Ageing, University of Birmingham, Birmingham, UK; NIHR Birmingham Biomedical Research Centre, University of Birmingham, Birmingham, UK.
² University Hospital Southampton NHS Foundation Trust, Southampton, UK.
³ The Royal Wolverhampton NHS Trust, Wolverhampton, UK.
⁴ Guy's, King's, & St Thomas' School of Medical Education, King's College London, London, UK.
⁵ Queen Elizabeth Hospital, University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK.
⁶ AI Centre for Value Based Healthcare, King's College London, London, UK; Birmingham Women's and Children's NHS Foundation Trust, Birmingham, UK.
⁷ Queen Elizabeth Hospital, University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK; University Hospitals of Leicester NHS Trust, Leicester, UK.
⁸ NIHR Birmingham Biomedical Research Centre, University of Birmingham, Birmingham, UK; Birmingham Health Partners Centre for Regulatory Science and Innovation, University of Birmingham, Birmingham, UK; NIHR Applied Research Collaboration (ARC) West Midlands, University of Birmingham, Birmingham, UK; Centre for Patient Reported Outcomes Research, Institute of Applied Health Research, College of Medical and Dental Sciences, University of Birmingham, Birmingham, UK; NIHR Blood and Transplant Research Unit (BTRU) in Precision Transplant and Cellular Therapeutics, Birmingham, UK.
⁹ Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA; Institute for Medical Engineering & Science, Massachusetts Institute of Technology, Cambridge, MA, USA.
¹⁰ Department of Bioethics, The Hospital for Sick Children, Toronto, ON, Canada; Genetics & Genome Biology, SickKids Research Institute, Toronto, ON, Canada.
¹¹ Institute of Inflammation and Ageing, University of Birmingham, Birmingham, UK; Hughes Hall, University of Cambridge, Cambridge, UK; Roche Diagnostics, Rotkreuz, Switzerland.
¹² Institute of Health Informatics, University College London, London, UK; PATH, Seattle, WA, USA; Wellcome Trust, London, UK.
¹³ Victor Phillip Dahdaleh Heart & Lung Research Institute, University of Cambridge, Cambridge, UK.
¹⁴ Independent Cancer Patients Voice, London, UK.
¹⁵ Oxford University Hospitals NHS Foundation Trust, Oxford, UK.
¹⁶ Queen Elizabeth Hospital, University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK; Institute of Inflammation and Ageing, University of Birmingham, Birmingham, UK; NIHR Birmingham Biomedical Research Centre, University of Birmingham, Birmingham, UK; Birmingham Women's and Children's NHS Foundation Trust, Birmingham, UK; NIHR Biomedical Research Centre, Moorfields Eye Hospital and University College London, London, UK.
¹⁷ Queen Elizabeth Hospital, University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK; Institute of Inflammation and Ageing, University of Birmingham, Birmingham, UK; NIHR Birmingham Biomedical Research Centre, University of Birmingham, Birmingham, UK. Electronic address: x.liu.8@bham.ac.uk.

PMID: 39455195
DOI: 10.1016/S2589-7500(24)00146-8

Free article

Revealing transparency gaps in publicly available COVID-19 datasets used for medical artificial intelligence development-a systematic review

Joseph E Alderman et al. Lancet Digit Health. 2024 Nov.

Free article

. 2024 Nov;6(11):e827-e847.

doi: 10.1016/S2589-7500(24)00146-8.

Authors

Affiliations

¹ Queen Elizabeth Hospital, University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK; Institute of Inflammation and Ageing, University of Birmingham, Birmingham, UK; NIHR Birmingham Biomedical Research Centre, University of Birmingham, Birmingham, UK.
² University Hospital Southampton NHS Foundation Trust, Southampton, UK.
³ The Royal Wolverhampton NHS Trust, Wolverhampton, UK.
⁴ Guy's, King's, & St Thomas' School of Medical Education, King's College London, London, UK.
⁵ Queen Elizabeth Hospital, University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK.
⁶ AI Centre for Value Based Healthcare, King's College London, London, UK; Birmingham Women's and Children's NHS Foundation Trust, Birmingham, UK.
⁷ Queen Elizabeth Hospital, University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK; University Hospitals of Leicester NHS Trust, Leicester, UK.
⁸ NIHR Birmingham Biomedical Research Centre, University of Birmingham, Birmingham, UK; Birmingham Health Partners Centre for Regulatory Science and Innovation, University of Birmingham, Birmingham, UK; NIHR Applied Research Collaboration (ARC) West Midlands, University of Birmingham, Birmingham, UK; Centre for Patient Reported Outcomes Research, Institute of Applied Health Research, College of Medical and Dental Sciences, University of Birmingham, Birmingham, UK; NIHR Blood and Transplant Research Unit (BTRU) in Precision Transplant and Cellular Therapeutics, Birmingham, UK.
⁹ Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA; Institute for Medical Engineering & Science, Massachusetts Institute of Technology, Cambridge, MA, USA.
¹⁰ Department of Bioethics, The Hospital for Sick Children, Toronto, ON, Canada; Genetics & Genome Biology, SickKids Research Institute, Toronto, ON, Canada.
¹¹ Institute of Inflammation and Ageing, University of Birmingham, Birmingham, UK; Hughes Hall, University of Cambridge, Cambridge, UK; Roche Diagnostics, Rotkreuz, Switzerland.
¹² Institute of Health Informatics, University College London, London, UK; PATH, Seattle, WA, USA; Wellcome Trust, London, UK.
¹³ Victor Phillip Dahdaleh Heart & Lung Research Institute, University of Cambridge, Cambridge, UK.
¹⁴ Independent Cancer Patients Voice, London, UK.
¹⁵ Oxford University Hospitals NHS Foundation Trust, Oxford, UK.
¹⁶ Queen Elizabeth Hospital, University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK; Institute of Inflammation and Ageing, University of Birmingham, Birmingham, UK; NIHR Birmingham Biomedical Research Centre, University of Birmingham, Birmingham, UK; Birmingham Women's and Children's NHS Foundation Trust, Birmingham, UK; NIHR Biomedical Research Centre, Moorfields Eye Hospital and University College London, London, UK.
¹⁷ Queen Elizabeth Hospital, University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK; Institute of Inflammation and Ageing, University of Birmingham, Birmingham, UK; NIHR Birmingham Biomedical Research Centre, University of Birmingham, Birmingham, UK. Electronic address: x.liu.8@bham.ac.uk.

PMID: 39455195
DOI: 10.1016/S2589-7500(24)00146-8

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.

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Conflict of interest statement

Declaration of interests JEA is a named researcher on grants from the Medical Research Council (MRC) and the Engineering & Physical Sciences Research Council with payments made to his institution for the delivery of other research projects; and is the co-organiser of Alan Turing Institute Clinical AI interest group (unpaid). ELe has received grants from the NHS AI Lab. MCa has received grants from the National Institute for Health and Care Research (NIHR), Health Data Research UK (HDR-UK), Innovate UK, Macmillan Cancer Support, GlaxoSmithKline, UCB Pharma, Research England as part of United Kingdom Research and Innovation (UKRI), European Commission, European Federation of Pharmaceutical Industries and Associations, Brain Tumour Charity, Gilead, Janssen, UKRI, and Merck for the delivery of other research projects; has received payment for delivering lectures from the University of Maastricht; has received a speaker fee from Cochrane Portugal; payments for reviewing from the South-Eastern Norway Regional Health Authority and Singapore National Medical Research Council; consulting fees from Aparito, CIS Oncology, Takeda, Merck, Daiichi Sankyo, Glaukos, GlaxoSmithKline, Patient-Centered Outcomes Research Institute, Genentech, Vertex, ICON, Halfloop, and Pfizer; and is associated with Proteus Consortium. MG has received grants from the Canadian Institute for Advanced Research, Helmsley Trust, Wellcome Trust, Moore Foundation, Volkswagen Foundation, I-Clinic, IBM-AI, Janssen research and development, Takeda, Quanta Computing, and Microsoft Research; and has acted as an advisor for the Symposium on Artificial Intelligence for Learning Health Systems and Conference on Health, Inference, and Learning. MDM has received grants from the Canadian Institutes of Health Research, AMS Healthcare, and the SickKids Foundation. JO works or has previously worked with AdvaMed AI Framework Group member (unpaid), AdvaMed Software Working Group member (unpaid), MedTech Europe AI Working Group member (unpaid), and MedTech Europe Digital Health Committee member (unpaid). CS has received grants from NIHR, UKRI, MRC, and NIHR Cambridge Biomedical Research Centre. RNM has received grants from MRC, British Heart Foundation, United States Agency for International Development, and HDR-UK. AKD has received grants from MRC and NIHR. XL has received grants from NIHR, Wellcome Trust, Research England, Moorfields Eye Hospital Charity; has received consulting fees from Hardian Health; has received payment or honoraria from the University of Turku; and is employed by Apple as a health scientist. All other authors declare no competing interests.

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Revealing transparency gaps in publicly available COVID-19 datasets used for medical artificial intelligence development-a systematic review

Affiliations

Revealing transparency gaps in publicly available COVID-19 datasets used for medical artificial intelligence development-a systematic review

Authors

Affiliations

Abstract

Conflict of interest statement

References

Publication types

MeSH terms

LinkOut - more resources

Full Text Sources

Medical