The DOI matches its Crossref record; 62 of the 62 references it cites were found in a registry or an index.
A review of existing and emerging digital technologies to combat the global trade in fake medicines
Abstract
INTRODUCTION: The globalization of the pharmaceutical supply chain has introduced new challenges, chief among them, fighting the international criminal trade in fake medicines. As the manufacture, supply, and distribution of drugs becomes more complex, so does the need for innovative technology-based solutions to protect patients globally. Areas covered: We conducted a multidisciplinary review of the science/health, information technology, computer science, and general academic literature with the aim of identifying cutting-edge existing and emerging 'digital' solutions to combat fake medicines. Our review identified five distinct categories of technology including mobile, radio frequency identification, advanced computational methods, online verification, and blockchain technology. Expert opinion: Digital fake medicine solutions are unifying platforms that integrate different types of anti-counterfeiting technologies as complementary solutions, improve information sharing and data collection, and are designed to overcome existing barriers of adoption and implementation. Investment in this next generation technology is essential to ensure the future security and integrity of the global drug supply chain.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
null (2016) · Philosophy Study · doi:10.17265/2159-5313/2016.09.003 · Cited by 28,317
Predicting the future - big data, machine learning, and clinical medicine
Obermeyer, Z., & Emanuel, E. J. (2016) · New England Journal of Medicine · doi:10.1056/nejmp1606181 · Cited by 3,652
The International Telecommunication Union
Jones, R. W. (2002) · IEEE Antennas and Propagation Society International Symposium 1997. Digest · doi:10.1109/aps.1997.631791 · Cited by 1,909
Blockchain technology in healthcare: The revolution starts here
Mettler, M. (2016) · 2016 IEEE 18th International Conference on e-Health Networking, Applications and Services (Healthcom) · doi:10.1109/healthcom.2016.7749510 · Cited by 1,237
The High Cost of Prescription Drugs in the United States: Origins and Prospects for Reform
Kesselheim, A. S., Avorn, J., & Sarpatwari, A. (2016) · JAMA · doi:10.1001/jama.2016.11237 · Cited by 592
Salathé, M., Bengtsson, L., Bodnar, T. J., Brewer, D. D., Brownstein, J. S., Buckee, C., Campbell, E. M., Cattuto, C., Khandelwal, S., Mabry, P. L., et al. (2012) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002616 · Cited by 500
Poor-quality antimalarial drugs in southeast Asia and sub-Saharan Africa
Nayyar, G. M., Breman, J. G., Newton, P. N., & Herrington, J. (2012) · The Lancet Infectious Diseases · doi:10.1016/s1473-3099(12)70064-6 · Cited by 410
Self-citationInkjet printing of upconversion nanoparticles for anti-counterfeit applications
You, M., Zhong, J., Yuan, H., Duan, Z., Lin, M., & Xu, F. (2015) · Nanoscale · doi:10.1039/c4nr06944g · Cited by 388
Application of the near-infrared spectroscopy in the pharmaceutical technology
Jamrógiewicz, M. (2012) · Journal of Pharmaceutical and Biomedical Analysis · doi:10.1016/j.jpba.2012.03.009 · Cited by 299
Big Data Opportunities for Global Infectious Disease Surveillance
Hay, S. I., George, D. B., Moyes, C. L., & Brownstein, J. S. (2013) · PLoS Medicine · doi:10.1371/journal.pmed.1001413 · Cited by 288
Cited by
Dutta, P., Choi, T.-M., Somani, S., & Butala, R. (2020) · Transportation Research Part E: Logistics and Transportation Review · doi:10.1016/j.tre.2020.102067 · Cited by 1,545
Blockchain technology in healthcare: a systematic review
Agbo, C. C., Mahmoud, Q. H., & Eklund, J. M. (2019) · Healthcare · doi:10.3390/healthcare7020056 · Cited by 1,131
Pournader, M., Shi, Y., Seuring, S., & Koh, S. C. L. (2020) · International Journal of Production Research · doi:10.1080/00207543.2019.1650976 · Cited by 854
A Systematic Review of the Use of Blockchain in Healthcare
Hölbl, M., Kompara, M., Kamišalić, A., & Nemec Zlatolas, L. (2018) · Symmetry · doi:10.3390/sym10100470 · Cited by 780
The power of a blockchain-based supply chain
Azzi, R., Chamoun, R. K., & Sokhn, M. (2019) · Computers & Industrial Engineering · doi:10.1016/j.cie.2019.06.042 · Cited by 624
Metadata
- Authors
- Mackey, T. K., & Nayyar, G.
- Venue
- Expert Opinion on Drug Safety
- Publisher
- Informa UK Limited
- Published
- 2017-04-07
- Volume
- 16
- Issue
- 5
- Pages
- 587-602
- ISSN
- 1474-0338 (ISSN-L)
- 1744-764X
- OpenAlex
- W2602679156
- Cited by
- 361
- References
- 62
- FWCI
- 27.81
- Type
- journal article
- Language
- en
- Open access
- closed
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.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: A review of existing and emerging digital technologies to combat the global trade in fake medicines. https://app.cochat.ai/papers/10.1080/14740338.2017.1313227
- The paper
- Mackey, T. K., & Nayyar, G. (2017). A review of existing and emerging digital technologies to combat the global trade in fake medicines. Expert Opinion on Drug Safety. https://doi.org/10.1080/14740338.2017.1313227
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
