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Beyond the hype of big data and artificial intelligence: building foundations for knowledge and wisdom
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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 [27].”
From Leading with AI in critical care nursing: challenges, opportunities, and the human factor (2024) · cites this paper as [27]
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“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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Roth, G. A., Abate, D., Abate, K. H., Abay, S. M., Abbafati, C., Abbasi, N., Abbastabar, H., Abd-Allah, F., Abdela, J., Abdelalim, A., et al. (2018) · The Lancet · doi:10.1016/s0140-6736(18)32203-7 · Cited by 8,600
Kyu, H. H., Abate, D., Abate, K. H., Abay, S. M., Abbafati, C., Abbasi, N., Abbastabar, H., Abd-Allah, F., Abdela, J., Abdelalim, A., et al. (2018) · The Lancet · doi:10.1016/s0140-6736(18)32335-3 · Cited by 4,192
RE-AIM Planning and Evaluation Framework: Adapting to New Science and Practice With a 20-Year Review
Glasgow, R. E., Harden, S. M., Gaglio, B., Rabin, B., Smith, M. L., Porter, G. C., Ory, M. G., & Estabrooks, P. A. (2019) · Frontiers in Public Health · doi:10.3389/fpubh.2019.00064 · Cited by 2,528
Welcome to Implementation Science
Eccles, M. P., & Mittman, B. S. (2006) · Implementation Science · doi:10.1186/1748-5908-1-1 · Cited by 2,254
The Inevitable Application of Big Data to Health Care
Murdoch, T. B., & Detsky, A. S. (2013) · JAMA · doi:10.1001/jama.2013.393 · Cited by 1,676
Predicting cardiovascular risk in England and Wales: prospective derivation and validation of QRISK2
Hippisley-Cox, J., Coupland, C., Vinogradova, Y., Robson, J., Minhas, R., Sheikh, A., & Brindle, P. (2008) · BMJ · doi:10.1136/bmj.39609.449676.25 · Cited by 1,459
Self-citationData Sharing by Scientists: Practices and Perceptions
Tenopir, C., Allard, S., Douglass, K., Aydinoglu, A. U., Wu, L., Read, E., Manoff, M., & Frame, M. (2011) · PLoS ONE · doi:10.1371/journal.pone.0021101 · Cited by 1,432
Practice-based research--“Blue Highways” on the NIH roadmap
Westfall, J. M., Mold, J., & Fagnan, L. (2007) · JAMA · doi:10.1001/jama.297.4.403 · Cited by 1,209
Predictive, personalized, preventive, participatory (P4) cancer medicine
Hood, L., & Friend, S. H. (2011) · Nature Reviews Clinical Oncology · doi:10.1038/nrclinonc.2010.227 · Cited by 953
Charting a course for genomic medicine from base pairs to bedside
Green, E. D., & Guyer, M. S. (2011) · Nature · doi:10.1038/nature09764 · Cited by 914
Research works printed but not linked: 1
- Ackoff RL. From data to wisdom. J Appl Syst Anal. 1989;16(1989):3–9.
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Wambua, A., Maurice, P. M., & Munyao, M. (2025) · Discover Education · doi:10.1007/s44217-025-00607-2 · Cited by 2
Kandara, O., & Kennedy, E. (2019) · Advances in educational technologies and instructional design book series · doi:10.4018/978-1-7998-1173-2.ch001 · Cited by 2
Huang, J., & Dang, F. (2022) · Journal of Healthcare Engineering · doi:10.1155/2022/2612074 · Cited by 2
RetractedAshkani‐Esfahani, S., & Ebrahimi, A. (2022) · Journal of Foot and Ankle Surgery (Asia Pacific) · doi:10.5005/jp-journals-10040-1266 · Cited by 2
Personality and Individual Differences
Ellis, D. A. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108671408.006 · Cited by 2
Ellis, D. A. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108671408.003 · Cited by 2
Ndemo, B. (2025) · Research Square · doi:10.21203/rs.3.rs-6572611/v1 · Cited by 2
Complexity of Patient Data in Primary Care Practice
Švab, I. (2020) · Elsevier eBooks · doi:10.1016/b978-0-12-801238-3.11590-9 · Cited by 1
Evidence and clinical trials in Endourology: where are we going
Donaldson, J. F., & McClinton, S. (2021) · Current Opinion in Urology · doi:10.1097/mou.0000000000000851 · Cited by 1
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Fang, N., Pluyter, J. R., Bakker, S., Jacobs, I., Luyer, M. D. P., Nederend, J., Raijmakers, J., Chen, L.-L., & Funk, M. (2024) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3613905.3650779 · Cited by 1
Perspective Chapter: The Role of Artificial Intelligence in Medical Curriculum Design
Naeim, M. (2026) · Education and human development · doi:10.5772/intechopen.1012732 · Cited by 2
Clinical performance of automated machine learning: A systematic review
Thirunavukarasu, A. J., Elangovan, K., Gutiérrez, L., Hassan, R., Li, Y., Tan, T. F., Cheng, H., Teo, Z. L., Lim, G., & Ting, D. S. W. (2024) · Annals of the Academy of Medicine Singapore · doi:10.47102/https://doi.org/10.47102/annals-acadmedsg.2023113 · Cited by 18
The Ethics of AI in Medical Research: A Call for Open and Honest Discussion
Tahseen, S., Memon, M. A., Kamran, S. M., & Pathan, K. T. (2024) · Liaquat Medical Research Journal · doi:10.38106/lmrj.2024.6.2-07 · Cited by 1
Grant, N. V., Bejan, A., Kunze, C., & Burkhardt, H. (2024) · Mensch und Computer (MuC) · doi:10.1145/3670653.3677483 · Cited by 1
RETRACTED: Lightweight multiparty privacy set intersection protocol for internet of medical things
Shan, Z., Zhang, L., Wu, Q., & Rezaeibagha, F. (2025) · Journal of Industrial Information Integration · doi:10.1016/j.jii.2025.100863 · Cited by 1
Retracted
Metadata
- 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
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