Artificial Intelligence-Enabled Nanomedicine in Precision Oncology: Diagnosis, Targeted Therapy, and Clinical Translation.
4 affiliations
- 1Department of Orthopedics, The Second Affiliated Hospital of Hainan Medical University, Hainan, Haikou, 570311, People's Republic of China.
- 2Department of Rehabilitation, The Second Affiliated Hospital of Hainan Medical University, Hainan, Haikou, 570311, People's Republic of China.
- 3Department of Medical Biotechnology, Faculty of Medicine, Kermanshah University of Medical Sciences, Kermanshah, Iran.
- 4Department of Medical Laboratory Sciences, Chabahar University of Medical Sciences, Chabahar, Iran.
Abstract
Cancer nanomedicine offers programmable platforms for diagnosis, imaging, drug and gene delivery, and theranostics, but its clinical performance is constrained by tumor heterogeneity, complex nano-bio interactions, and interpatient variability. Artificial intelligence (AI) provides a means to integrate physicochemical, biological, imaging, molecular, and clinical data to improve the design and application of nanoscale cancer technologies. This Review examines the convergence of AI and nanotechnology across cancer diagnosis, therapeutic selection, treatment optimization, monitoring, drug development, and clinical translation, with particular emphasis on materials design and biological performance. AI-based approaches can support early detection, patient stratification, response and toxicity prediction, and rational therapeutic development. In parallel, nanoplatforms, including liposomes, polymeric nanoparticles, dendrimers, metallic and inorganic nanoparticles, biomimetic systems, and stimuli-responsive carriers, can improve pharmacokinetics, enhance tumor exposure, reduce systemic toxicity, and enable controlled delivery of diverse payloads. Their integration may advance precision nanomedicine by guiding nanoparticle composition, size, surface properties, targeting ligands, biodistribution, tumor accumulation, cellular uptake, protein-corona formation, release kinetics, toxicity, and therapeutic response. Conversely, nanodiagnostic and nanotheranostic systems can generate imaging, molecular, pharmacokinetic, and response data for AI-assisted patient selection and adaptive treatment monitoring. Translation will require standardized datasets, reproducible nanoparticle characterization, scalable manufacturing, explainable and externally validated models, regulatory clarity, long-term safety assessment, and equitable implementation. Together, AI-guided nanotechnology provides a framework linking materials design, biological interactions, and patient-specific cancer therapy.
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BibTeX
@article{altahla2026artificial,
title = {{Artificial Intelligence-Enabled Nanomedicine in Precision Oncology: Diagnosis, Targeted Therapy, and Clinical Translation.}},
author = {Ruba Altahla and Yongping Wang and Mahdi Morshedi Yekta and Zhaowei Wang and Huiyi Jiang and Chaoyi Li and Pijun Zhang and Mansoor Bolideei and Mohammad Javad Mehran and Jamal Alshorman},
journal = {International journal of nanomedicine},
year = {2026},
month = oct,
doi = {10.2147/ijn.s644095},
url = {https://europepmc.org/article/MED/42974481},
note = {CC BY-NC},
}
RIS
TY - JOUR
TI - Artificial Intelligence-Enabled Nanomedicine in Precision Oncology: Diagnosis, Targeted Therapy, and Clinical Translation.
AU - Ruba Altahla
AU - Yongping Wang
AU - Mahdi Morshedi Yekta
AU - Zhaowei Wang
AU - Huiyi Jiang
AU - Chaoyi Li
AU - Pijun Zhang
AU - Mansoor Bolideei
AU - Mohammad Javad Mehran
AU - Jamal Alshorman
PY - 2026
DA - 2026/10/29
JO - International journal of nanomedicine
DO - 10.2147/ijn.s644095
UR - https://europepmc.org/article/MED/42974481
AB - Cancer nanomedicine offers programmable platforms for diagnosis, imaging, drug and gene delivery, and theranostics, but its clinical performance is constrained by tumor heterogeneity, complex nano-bio interactions, and interpatient variability. Artificial intelligence (AI) provides a means to integrate physicochemical, biological, imaging, molecular, and clinical data to improve the design and application of nanoscale cancer technologies. This Review examines the convergence of AI and nanotechnology across cancer diagnosis, therapeutic selection, treatment optimization, monitoring, drug development, and clinical translation, with particular emphasis on materials design and biological performance. AI-based approaches can support early detection, patient stratification, response and toxicity prediction, and rational therapeutic development. In parallel, nanoplatforms, including liposomes, polymeric nanoparticles, dendrimers, metallic and inorganic nanoparticles, biomimetic systems, and stimuli-responsive carriers, can improve pharmacokinetics, enhance tumor exposure, reduce systemic toxicity, and enable controlled delivery of diverse payloads. Their integration may advance precision nanomedicine by guiding nanoparticle composition, size, surface properties, targeting ligands, biodistribution, tumor accumulation, cellular uptake, protein-corona formation, release kinetics, toxicity, and therapeutic response. Conversely, nanodiagnostic and nanotheranostic systems can generate imaging, molecular, pharmacokinetic, and response data for AI-assisted patient selection and adaptive treatment monitoring. Translation will require standardized datasets, reproducible nanoparticle characterization, scalable manufacturing, explainable and externally validated models, regulatory clarity, long-term safety assessment, and equitable implementation. Together, AI-guided nanotechnology provides a framework linking materials design, biological interactions, and patient-specific cancer therapy.
SN - 1176-9114
SN - 1178-2013
ER -