Cited by 501Nanoplatforms for cancer theranosticsNanoparticle-Based Drug DeliveryNanotechnology research and applications

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Integrating Artificial Intelligence and Nanotechnology for Precision Cancer Medicine

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Adir, Omer, Poley, Maria, Chen, Gal, Froim, Sahar, Krinsky, Nitzan, Shklover, Jeny, Shainsky‐Roitman, Janna, Lammers, Twan, Schroeder, Avi·2020·Advanced Materials

DOI 10.1002/adma.201901989PMC PMC7124889

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Abstract

Artificial intelligence (AI) and nanotechnology are two fields that are instrumental in realizing the goal of precision medicine—tailoring the best treatment for each cancer patient. Recent conversion between these two fields is enabling better patient data acquisition and improved design of nanomaterials for precision cancer medicine. Diagnostic nanomaterials are used to assemble a patient‐specific disease profile, which is then leveraged, through a set of therapeutic nanotechnologies, to improve the treatment outcome. However, high intratumor and interpatient heterogeneities make the rational design of diagnostic and therapeutic platforms, and analysis of their output, extremely difficult. Integration of AI approaches can bridge this gap, using pattern analysis and classification algorithms for improved diagnostic and therapeutic accuracy. Nanomedicine design also benefits from the application of AI, by optimizing material properties according to predicted interactions with the target drug, biological fluids, immune system, vasculature, and cell membranes, all affecting therapeutic efficacy. Here, fundamental concepts in AI are described and the contributions and promise of nanotechnology coupled with AI to the future of precision cancer medicine are reviewed.

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Potent and specific genetic interference by double-stranded RNA in Caenorhabditis elegans
Fire, A., Xu, S., Montgomery, M. K., Kostas, S. A., Driver, S. E., & Mello, C. C. (1998) · Nature · doi:10.1038/35888 · Cited by 15,429
Duplexes of 21-nucleotide RNAs mediate RNA interference in cultured mammalian cells
Elbashir, S. M., Harborth, J., Lendeckel, W., Yalcin, A., Weber, K., & Tuschl, T. (2001) · Nature · doi:10.1038/35078107 · Cited by 9,456
Intratumor Heterogeneity and Branched Evolution Revealed by Multiregion Sequencing
Gerlinger, M., Rowan, A. J., Horswell, S., Larkin, J., Endesfelder, D., Gronroos, E., Martinez, P., Matthews, N., Stewart, A., Tarpey, P., et al. (2012) · New England Journal of Medicine · doi:10.1056/nejmoa1113205 · Cited by 7,774
Stimuli-responsive nanocarriers for drug delivery
Mura, S., Nicolas, J., & Couvreur, P. (2013) · Nature Materials · doi:10.1038/nmat3776 · Cited by 6,289
Cancer nanomedicine: progress, challenges and opportunities
Shi, J., Kantoff, P. W., Wooster, R., & Farokhzad, O. C. (2017) · Nature Reviews Cancer · doi:10.1038/nrc.2016.108 · Cited by 5,873
Development and Applications of CRISPR-Cas9 for Genome Engineering
Hsu, P. D., Lander, E. S., & Zhang, F. (2014) · Cell · doi:10.1016/j.cell.2014.05.010 · Cited by 5,762
A New Initiative on Precision Medicine
Collins, F. S., & Varmus, H. (2015) · New England Journal of Medicine · doi:10.1056/nejmp1500523 · Cited by 5,202
Fully integrated wearable sensor arrays for multiplexed in situ perspiration analysis
Gao, W., Emaminejad, S., Nyein, H. Y. Y., Challa, S., Chen, K., Peck, A., Fahad, H. M., Ota, H., Shiraki, H., Kiriya, D., et al. (2016) · Nature · doi:10.1038/nature16521 · Cited by 5,087
Circulating Tumor Cells, Disease Progression, and Survival in Metastatic Breast Cancer
Cristofanilli, M., Budd, G. T., Ellis, M. J., Stopeck, A., Matera, J., Miller, M. C., Reuben, J. M., Doyle, G. V., Allard, W. J., Terstappen, L. W. M. M., et al. (2004) · New England Journal of Medicine · doi:10.1056/nejmoa040766 · Cited by 4,602
Artificial intelligence in radiology
Hosny, A., Parmar, C., Quackenbush, J., Schwartz, L. H., & Aerts, H. J. W. L. (2018) · Nature Reviews Cancer · doi:10.1038/s41568-018-0016-5 · Cited by 3,803

Show all 179 references

Research works printed but not linked: 4

Korlach J., 2010, Methods in Enzymology, 431
Tan J., 2015, Biocomput, 132
Harrington K. J., 2001, Clin. Cancer Res., 7, 243
Jeffery M., 2016, Cochrane Database Syst. Rev., 11, CD002200

Sorted by citations

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Mendes, B. B., Conniot, J., Avital, A., Yao, D., Jiang, X., Zhou, X., Sharf-Pauker, N., Xiao, Y., Adir, O., Liang, H., et al. (2022) · Nature Reviews Methods Primers · doi:10.1038/s43586-022-00104-y · Cited by 719
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Younis, M. A., Tawfeek, H. M., Abdellatif, A. A. H., Abdel-Aleem, J. A., & Harashima, H. (2021) · Advanced Drug Delivery Reviews · doi:10.1016/j.addr.2021.114083 · Cited by 506
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Dlamini, Z., Francies, F. Z., Hull, R., & Marima, R. M. (2020) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2020.08.019 · Cited by 419
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Li, J., Jiang, X., Li, H., Gelinsky, M., & Gu, Z. (2021) · Advanced Materials · doi:10.1002/adma.202004172 · Cited by 406
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Patel, S., Kim, J., Herrera‐Barrera, M., Mukherjee, A., Kabanov, A. V., & Sahay, G. (2019) · Advanced Drug Delivery Reviews · doi:10.1016/j.addr.2019.08.004 · Cited by 409
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Current approaches of nanomedicines in the market and various stage of clinical translation
Shan, X., Gong, X., Li, J., Wen, J., Li, Y., & Zhang, Z. (2022) · Acta Pharmaceutica Sinica B · doi:10.1016/j.apsb.2022.02.025 · Cited by 354
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Kemp, J. A., & Kwon, Y. J. (2021) · Nano Convergence · doi:10.1186/s40580-021-00282-7 · Cited by 290
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Baccour, E., Mhaisen, N., Abdellatif, A. A., Erbad, A., Mohamed, A. M. S., Hamdi, M., & Guizani, M. (2022) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2022.3200740 · Cited by 223
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Dabbagh, S. R., Sarabi, M. R., Birtek, M. T., Seyfi, S., Sitti, M., & Taşoğlu, S. (2022) · Nature Communications · doi:10.1038/s41467-022-33409-3 · Cited by 196
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Mazumdar, H., Khondakar, K. R., Das, S., Halder, A., & Kaushik, A. K. (2024) · Expert Opinion on Drug Delivery · doi:10.1080/17425247.2024.2440618 · Cited by 180
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Smith, B. R., & Edelman, E. R. (2023) · Nature Cardiovascular Research · doi:10.1038/s44161-023-00232-y · Cited by 156
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Masson, J., Biggins, J. S., & Ringe, E. (2023) · Nature Nanotechnology · doi:10.1038/s41565-022-01284-0 · Cited by 155
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Nag, S., Mitra, O., Tripathi, G., Adur, I., Mohanto, S., Nama, M., Samanta, S., Gowda, B. H. J., Subramaniyan, V., Sundararajan, V., et al. (2024) · Photodiagnosis and Photodynamic Therapy · doi:10.1016/j.pdpdt.2023.103959 · Cited by 132
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Shirzad, M., Shaban, M., Mohammadzadeh, V., Rahdar, A., Fathi‐karkan, S., Hoseini, Z. S., Najafi, M., Kharaba, Z. J., & Aboudzadeh, M. A. (2025) · BioNanoScience · doi:10.1007/s12668-025-01980-w · Cited by 107
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Fan, Z., Jiang, C., Wang, Y., Wang, K., Marsh, J., Zhang, D., Chen, X., & Nie, L. (2022) · Nanoscale Horizons · doi:10.1039/d2nh00070a · Cited by 103
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Robust Superhydrophobic rGO/PPy/PDMS Coatings on a Polyurethane Sponge for Underwater Pressure and Temperature Sensing
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Harnessing nanotechnology for cancer treatment
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Wang, Z., Yang, J., Qin, G., Zhao, C., Ren, J., & Qu, X. (2022) · Angewandte Chemie International Edition · doi:10.1002/anie.202204291 · Cited by 82
Illuminating the future of precision cancer surgery with fluorescence imaging and artificial intelligence convergence
Cheng, H., Xu, H.-T., Peng, B., Huang, X., Hu, Y., Zheng, C., & Zhang, Z. (2024) · npj Precision Oncology · doi:10.1038/s41698-024-00699-3 · Cited by 87
Emerging trends in the nanomedicine applications of functionalized magnetic nanoparticles as novel therapies for acute and chronic diseases
Dash, S., Das, T., Patel, P., Panda, P. K., Suar, M., & Verma, S. K. (2022) · Journal of Nanobiotechnology · doi:10.1186/s12951-022-01595-3 · Cited by 80
Intelligent Nanoparticle-Based Dressings for Bacterial Wound Infections
Jiang, L., & Loo, S. C. J. (2020) · ACS Applied Bio Materials · doi:10.1021/acsabm.0c01168 · Cited by 77
Expanding the Horizons of Machine Learning in Nanomaterials to Chiral Nanostructures
Kuznetsova, V. A., Coogan, Á., Botov, D., Gromova, Y. A., Ushakova, E. V., & Gun’ko, Y. K. (2024) · Advanced Materials · doi:10.1002/adma.202308912 · Cited by 78
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Wang, Q., Liu, Y., Li, C., Xu, S., Xu, S., & Liu, B. (2025) · Advanced Science · doi:10.1002/advs.202503138 · Cited by 78
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Shan, X., Cai, Y., Zhu, B., Zhou, L., Sun, X., Xu, X., Yin, Q., Wang, D., & Li, Y. (2024) · Nature Communications · doi:10.1038/s41467-024-54265-3 · Cited by 81
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Strategies of Artificial intelligence tools in the domain of nanomedicine
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Taha, T. B., Barzinjy, A. A., Hussain, F. H. S., & Nurtayeva, T. (2022) · Memories - Materials Devices Circuits and Systems · doi:10.1016/j.memori.2022.100011 · Cited by 64
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Valizadeh, A., Khaleghi, A. A., Alipanah, H., Zarenezhad, E., & Osanloo, M. (2021) · BioNanoScience · doi:10.1007/s12668-021-00880-z · Cited by 63
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Meng, T., He, D., Han, Z., Shi, R., Wang, Y., Ren, B., Zhang, C., Mao, Z., Luo, G., & Deng, J. (2024) · Nano-Micro Letters · doi:10.1007/s40820-024-01455-9 · Cited by 63
Cancer chemoprevention: signaling pathways and strategic approaches
Ren, J., Yan, G., Yang, L., Kong, L., Guan, Y., Sun, H., Liu, C., Liu, L., Han, Y., & Wang, X. (2025) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-025-02167-1 · Cited by 67
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Authors: Adir, O., Poley, M., Chen, G., Froim, S., Krinsky, N., Shklover, J., Shainsky‐Roitman, J., Lammers, T., & Schroeder, A.
Affiliations: Technion – Israel Institute of Technology (IL)
Tel Aviv University (IL)
Universitätsklinikum Aachen (DE)
Corresponding author: Schroeder, Avi
ORCID: Adir, Omer
Poley, Maria
Chen, Gal
Krinsky, Nitzan
Shklover, Jeny
Lammers, Twan
Schroeder, Avi
Venue: Advanced Materials
Publisher: Wiley
Published: 2019-07-09
Volume: 32
Issue: 13
Pages: e1901989-e1901989
ISSN: 0935-9648 (ISSN-L)
1521-4095
DOI: 10.1002/adma.201901989
PMC: PMC7124889
OpenAlex: W2957366757
Cited by: 496
References: 179
Citations by year: 2026: 69 · 2025: 113 · 2024: 111 · 2023: 70 · 2022: 54 · 2021: 49 · 2020: 29 · 2019: 1
FWCI: 2.67
Type: journal article
Language: en
Open access: green
www.ncbi.nlm.nih.gov
Licence: onlinelibrary.wiley.com/termsAndConditions#vor (vor)
Locations: Advanced Materials (publishedVersion)
PubMed (publishedVersion)
PubMed Central (acceptedVersion)
RWTH Publications (RWTH Aachen) (submittedVersion)
Funders: European Research Council (STG‐2015‐680242)
Russell Berrie Nanotechnology Institute, Technion-Israel Institute of Technology
Lorry I. Lokey Interdisciplinary Center for Life Sciences and Engineering, Technion-Israel Institute of Technology
Ministry of Economy (52752)
Ministry of Science and Technology, Israel (3‐11878)
Israel Science Foundation (1778/13, 1421/17)
Israel Cancer Association (2015‐0116)
German-Israeli Foundation for Scientific Research and Development (I‐2328‐1139.10/2012)
FP7 Science in Society (908049)
Phospholipid Research Center
Topics: Physical Sciences › Engineering › Biomedical Engineering › Nanoplatforms for cancer theranostics
Physical Sciences › Materials Science › Biomaterials › Nanoparticle-Based Drug Delivery
Physical Sciences › Materials Science › General Materials Science › Nanotechnology research and applications
Keywords: artificial intelligence, nanotechnology, precision oncology, nanomedicine, intratumoral heterogeneity, interpatient heterogeneity, machine learning classification
MeSH terms: Animals, Artificial Intelligence, Humans, Neoplasms, Drug Delivery Systems, Computational Biology, Nanotechnology, Nanostructures, Nanomedicine, Precision Medicine

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Integrating Artificial Intelligence and Nanotechnology for Precision Cancer Medicine

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