The DOI matches its Crossref record; 19 of the 19 references it cites were found in a registry or an index.
Artificial intelligence aids in development of nanomedicines for cancer management
Abstract
Over the last decade, the nanomedicine has experienced unprecedented development in diagnosis and management of diseases. A number of nanomedicines have been approved in clinical use, which has demonstrated the potential value of clinical transition of nanotechnology-modified medicines from bench to bedside. The application of artificial intelligence (AI) in development of nanotechnology-based products could transform the healthcare sector by realizing acquisition and analysis of large datasets, and tailoring precision nanomedicines for cancer management. AI-enabled nanotechnology could improve the accuracy of molecular profiling and early diagnosis of patients, and optimize the design pipeline of nanomedicines by tuning the properties of nanomedicines, achieving effective drug synergy, and decreasing the nanotoxicity, thereby, enhancing the targetability, personalized dosing and treatment potency of nanomedicines. Herein, the advances in AI-enabled nanomedicines in cancer management are elaborated and their application in diagnosis, monitoring and therapy as well in precision medicine development is discussed.
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
Sung, H., Ferlay, J., Siegel, R. L., Laversanne, M., Soerjomataram, I., Jemal, A., & Bray, F. (2021) · CA: A Cancer Journal for Clinicians · doi:10.3322/caac.21660 · Cited by 108,699
Pembrolizumab plus chemotherapy in metastatic non–small-cell lung cancer
Gandhi, L., Rodríguez-Abreu, D., Gadgeel, S., Esteban, E., Felip, E., De Angelis, F., Domine, M., Clingan, P., Hochmair, M. J., Powell, S. F., et al. (2018) · New England Journal of Medicine · doi:10.1056/nejmoa1801005 · Cited by 6,607
Nivolumab plus Ipilimumab in Advanced Melanoma
Wolchok, J. D., Kluger, H., Callahan, M. K., Postow, M. A., Rizvi, N. A., Lesokhin, A. M., Segal, N. H., Ariyan, C. E., Gordon, R.-A., Reed, K., et al. (2013) · New England Journal of Medicine · doi:10.1056/nejmoa1302369 · Cited by 4,230
Sato, T., Stange, D. E., Ferrante, M., Vries, R. G. J., van Es, J. H., van den Brink, S., van Houdt, W. J., Pronk, A., van Gorp, J., Siersema, P. D., et al. (2011) · Gastroenterology · doi:10.1053/j.gastro.2011.07.050 · Cited by 3,753
Martinez, A. M., & Kak, A. C. (2001) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.908974 · Cited by 3,249
Performance of neural network basecalling tools for Oxford Nanopore sequencing
Wick, R. R., Judd, L. M., & Holt, K. E. (2019) · Genome Biology · doi:10.1186/s13059-019-1727-y · Cited by 3,221
Artificial intelligence in healthcare
Yu, K.-H., Beam, A. L., & Kohane, I. S. (2018) · Nature Biomedical Engineering · doi:10.1038/s41551-018-0305-z · Cited by 2,817
Artificial intelligence in medicine
Hamet, P., & Tremblay, J. (2017) · Metabolism · doi:10.1016/j.metabol.2017.01.011 · Cited by 2,334
Personal omics profiling reveals dynamic molecular and medical phenotypes
Chen, R., Mias, G. I., Li-Pook-Than, J., Jiang, L., Lam, H. Y. K., Chen, R., Miriami, E., Karczewski, K. J., Hariharan, M., Dewey, F. E., et al. (2012) · Cell · doi:10.1016/j.cell.2012.02.009 · Cited by 1,291
Ooft, S. N., Weeber, F., Dijkstra, K. K., McLean, C. M., Kaing, S., van Werkhoven, E., Schipper, L., Hoes, L., Vis, D. J., van de Haar, J., et al. (2019) · Science Translational Medicine · doi:10.1126/scitranslmed.aay2574 · Cited by 813
Integrated omics: tools, advances and future approaches
Misra, B. B., Langefeld, C., Olivier, M., & Cox, L. A. (2019) · Journal of Molecular Endocrinology · doi:10.1530/jme-18-0055 · Cited by 686
Preparation and application of pH-responsive drug delivery systems
Ding, H., Tan, P., Fu, S., Tian, X., Zhang, H., Ma, X., Gu, Z., & Luo, K. (2022) · Journal of Controlled Release · doi:10.1016/j.jconrel.2022.05.056 · Cited by 645
Self-citationThe Use of Alternative Strategies for Enhanced Nanoparticle Delivery to Solid Tumors
Izci, M., Maksoudian, C., Manshian, B. B., & Soenen, S. J. (2021) · Chemical Reviews · doi:10.1021/acs.chemrev.0c00779 · Cited by 506
Integrating Artificial Intelligence and Nanotechnology for Precision Cancer Medicine
Adir, O., Poley, M., Chen, G., Froim, S., Krinsky, N., Shklover, J., Shainsky‐Roitman, J., Lammers, T., & Schroeder, A. (2020) · Advanced Materials · doi:10.1002/adma.201901989 · Cited by 496
Kelley, S. O., Mirkin, C. A., Walt, D. R., Ismagilov, R. F., Toner, M., & Sargent, E. H. (2014) · Nature Nanotechnology · doi:10.1038/nnano.2014.261 · Cited by 398
Machine learning and integrative analysis of biomedical big data
Mirza, B., Wang, W., Wang, J., Choi, H., Chung, N. C., & Ping, P. (2019) · Genes · doi:10.3390/genes10020087 · Cited by 380
Tumor organoids: Opportunities and challenges to guide precision medicine
Veninga, V., & Voest, E. E. (2021) · Cancer Cell · doi:10.1016/j.ccell.2021.07.020 · Cited by 339
Barcoded nanoparticles for high throughput in vivo discovery of targeted therapeutics
Dahlman, J. E., Kauffman, K. J., Xing, Y., Shaw, T. E., Mir, F. F., Dlott, C. C., Langer, R., Anderson, D. G., & Wang, E. T. (2017) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1620874114 · Cited by 335
Nilsson, M. B., Sun, H., Robichaux, J., Pfeifer, M., McDermott, U., Travers, J., Diao, L., Xi, Y., Tong, P., Shen, L., et al. (2020) · Science Translational Medicine · doi:10.1126/scitranslmed.aaz4589 · Cited by 177
Cited by
Smart nanoparticles for cancer therapy
Sun, L., Liu, H., Ye, Y., Lei, Y., Islam, R., Tan, S., Tong, R., Miao, Y.-B., & Cai, L. (2023) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-023-01642-x · Cited by 1,073
Academic publisher guidelines on AI usage: A ChatGPT supported thematic analysis
Perkins, M., & Roe, J. (2024) · F1000Research · doi:10.12688/f1000research.142411.2 · Cited by 123
Artificial Intelligence Advancements in Oncology: A Review of Current Trends and Future Directions
Huhulea, E. N., Huang, L., Eng, S., Sumawi, B., Huang, A., Aifuwa, E., Hirani, R., Tiwari, R. K., & Etienne, M. (2025) · Biomedicines · doi:10.3390/biomedicines13040951 · Cited by 74
Samad, M. A., Ahmad, I., Zughaibi, T. A., Suhail, M., Zaidi, S. K., Al-Abbasi, F. A., & Tabrez, S. (2025) · European Journal of Medicinal Chemistry Reports · doi:10.1016/j.ejmcr.2025.100268 · Cited by 35
Metadata
- Authors
- Tan, P., Chen, X., Zhang, H., Wei, Q., & Luo, K.
- Venue
- Seminars in Cancer Biology
- Publisher
- Elsevier BV
- Published
- 2023-02
- Volume
- 89
- Pages
- 61-75
- ISSN
- 1044-579X (ISSN-L)
- OpenAlex
- W4317492689
- Cited by
- 246
- References
- 19
- FWCI
- 13.54
- 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
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.
