ReviewNPJ precision oncology2026
Comprehensive overview of AI methodologies in nano-drug delivery Optimization and Design.
Review in NPJ precision oncology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This review comprehensively examines the application of artificial intelligence (AI) to revolutionize precision oncology across all phases of drug development, including target discovery and molecular design, nanomedicine delivery, and resistance mitigation. Deep learning, systems biology, and multi-omics analytics enabled by AI accelerate target discovery, lead optimization, and personalized therapy. This study focuses on a new area of AI-driven nanocarrier design, adaptive therapy design, and digital twin clinical decision support. AI bridges the knowledge gap between molecular information and real-world data to enable forecasting, transparent, patient-centered cancer treatment, and the foundation for a data platform to counsel future generations of cancer patients, therapies, and resistance control.
Identifiers
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.