ReviewMedComm2026
Artificial Intelligence Integration With Nanotechnology for Cancer Therapy.
Review in MedComm, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Cancer nanomedicine offers a versatile platform for improving therapeutic index, but its clinical translation remains limited by unpredictable in vivo behavior, heterogeneous biological contexts, and inefficient design paradigms. Artificial intelligence (AI) is emerging as an integrative framework that links data-driven modeling with nanomedicine design, biological transport, and clinical decision-making. In this review, we discuss AI-guided strategies for material design, targeting, payload optimization, and in vivo delivery, with particular attention to protein corona-mediated biological identity, microenvironment-responsive activation, and biodistribution modeling. We further examine the translational requirements for AI-enabled nanomedicine, including data standardization, preclinical learning workflows, clinical stratification and risk-based governance. Finally, we outline future directions centered on transferable learning architectures, dynamic multiscale modeling and patient-aware therapeutic strategies. Together, these advances suggest that AI can move cancer nanomedicine beyond empirical formulation toward a more predictive, biologically informed, and clinically responsive discipline.
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What OpenQuestion 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 OpenQuestion graph.