Beyond the hype of big data and artificial intelligence: building foundations for knowledge and wisdom
Open accessAbstract
Big data, coupled with the use of advanced analytical approaches, such as artificial intelligence (AI), have the potential to improve medical outcomes and population health. Data that are routinely generated from, for example, electronic medical records and smart devices have become progressively easier and cheaper to collect, process, and analyze. In recent decades, this has prompted a substantial increase in biomedical research efforts outside traditional clinical trial settings. Despite the apparent enthusiasm of researchers, funders, and the media, evidence is scarce for successful implementation of products, algorithms, and services arising that make a real difference to clinical care. This article collection provides concrete examples of how "big data" can be used to advance healthcare and discusses some of the limitations and challenges encountered with this type of research. It primarily focuses on real-world data, such as electronic medical records and genomic medicine, considers new developments in AI and digital health, and discusses ethical considerations and issues related to data sharing. Overall, we remain positive that big data studies and associated new technologies will continue to guide novel, exciting research that will ultimately improve healthcare and medicine-but we are also realistic that concerns remain about privacy, equity, security, and benefit to all.
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The shifting roles and responsibilities identified in the study reflect a broader trend in healthcare where technology necessitates new skill sets and professional roles.
“The shifting roles and responsibilities identified in the study reflect a broader trend in healthcare where technology necessitates new skill sets and professional roles [27].”
From Leading with AI in critical care nursing: challenges, opportunities, and the human factor (2024) · cites this paper as [27]
Anonymizing data whenever feasible adds an extra layer of privacy, allowing educational insights without compromising individuals' identities.
“Anonymizing data whenever feasible adds an extra layer of privacy, allowing educational insights without compromising individuals’ identities [22].”
From Twelve tips for addressing ethical concerns in the implementation of artificial intelligence in medical education (2024) · cites this paper as [22]
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Retracted
- Authors
- Car, J., Sheikh, A., Wicks, P., & Williams, M. S.
- Affiliations
- Nanyang Technological University (SG)
- University of Edinburgh (GB)
- PatientsLikeMe (United States) (US)
- Corresponding author
- Wicks, Paul
- Venue
- BMC Medicine
- Published
- 2019-07-17
- Volume
- 17
- Issue
- 1
- Pages
- 143-143
- ISSN
- 1741-7015 (ISSN-L)
- PMC
- PMC6636050
- OpenAlex
- W2957812729
- Cited by
- 126
- References
- 34
- Citations by year
- 2026: 9 · 2025: 14 · 2024: 16 · 2023: 17 · 2022: 15 · 2021: 22 · 2020: 27 · 2019: 6
- FWCI
- 5.57
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- BMC Medicine (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- DR-NTU (Nanyang Technological University) (submittedVersion, cc-by)
- Edinburgh Research Explorer (University of Edinburgh) (submittedVersion, cc-by)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
- Keywords
- big data, artificial intelligence, real-world data, electronic health records, genomic medicine, digital health, data sharing ethics, privacy, data security, health equity, population health
- MeSH terms
- Big Data, Algorithms, Artificial Intelligence, Bioethics, Delivery of Health Care, Humans, Health Knowledge, Attitudes, Practice, Knowledge, Genomics, Information Dissemination, Biomedical Research, Electronic Health Records
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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