ReviewJournal of nanobiotechnology2025
AI-engineered multifunctional nanoplatforms: synergistically bridging precision diagnosis and intelligent therapy in next-generation oncology.
Review in Journal of nanobiotechnology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
7 citing papers in PubMed.
- Nanoparticles for Drug Delivery: Design, Mechanisms, and Clinical Translation.Molecules (Basel, Switzerland) · 2026Review
- Engineering smart polymeric lipid nanoparticles for breast cancer: AI-guided formulation design, biological barriers, and translational constraints.Journal of nanobiotechnology · 2026Review
- Polymer Nanoparticles in Medical Applications-Future Directions.Nanomaterials (Basel, Switzerland) · 2026Review
- Artificial intelligence empowered biomaterials for cancer therapy: From rational design to clinical translation.Chinese journal of cancer research = Chung-kuo yen cheng yen chiu · 2026Article
- Application Advances of Gold Nanoparticles in Cancer Theranostics: From Physicochemical Mechanisms to Multifunctional Nanoplatforms.International journal of molecular sciences · 2026Review
- Nanomaterial-Based Therapeutic Delivery: Integrating Redox Biology, Genetic Engineering, and Imaging-Guided Treatment.Antioxidants (Basel, Switzerland) · 2026Review
- Advancing Drug Discovery with AI: Machine and Deep Learning Strategies for Target Identification and Precision Nanomedicine.International journal of nanomedicine · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
The integration of artificial intelligence (AI) and nanotechnology is reshaping cancer diagnosis and treatment. In this context, intelligent nanoplatforms are multifunctional nanoscale systems designed or optimized with the help of AI, capable of combining tumor sensing, targeted delivery, controlled release, and adaptive response within a single platform. AI can analyze large-scale multi-omics and clinical datasets to support early cancer detection, accurate diagnosis, prognosis assessment, and refinement of personalized treatment strategies, while nanotechnology enables precise tumor targeting and site-specific drug delivery through diverse nanocarriers, thereby reducing systemic toxicity and improving therapeutic efficacy. Their interaction allows more rational nanomedicine design by optimizing key properties such as targeting capability, stability, and responsiveness, and nano-enabled imaging and sensing provide high-resolution data that further enhance model performance. Together, these advances point toward more personalized and efficient strategies for cancer diagnosis, therapy, and monitoring, although challenges related to data sharing, standardization, privacy, ethics, regulation, and development costs still need to be addressed for broader and safer clinical implementation.
Indexed as
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.