ReviewNanomedicine (London, England)2024
The role of artificial intelligence and data science in nanoparticles development: a review.
Review in Nanomedicine (London, England), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 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
8 citing papers in PubMed.
- A Review on Nanocarrier-Based Strategies for Sunitinib Delivery: Advances in Pharmacokinetic Enhancement and Targeted Theranostics.AAPS PharmSciTech · 2026Review
- Nanoparticles-enhanced CAR-T cell therapy: current advances and future directions.Biomarker research · 2026Review
- Multistep Machine Learning Pipeline For Polymeric Nanoparticle Design.AAPS PharmSciTech · 2025Article
- Genomic medicine in hepatology: mechanisms and liver treatment strategies.Molecular medicine (Cambridge, Mass.) · 2025Review
- Doxorubicin loaded polylactide nanoparticles functionalized histamine promote apoptosis of human gastric cancer cells AGS.Scientific reports · 2025Article
- Smart CAR-T Nanosymbionts: archetypes and proto-models.Frontiers in immunology · 2025Review
- Recent advances in the bench-to-bedside translation of cancer nanomedicines.Acta pharmaceutica Sinica. B · 2025Review
- Advancing gastric cancer treatment: nanotechnology innovations and future prospects.Cell biology and toxicology · 2024Review
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
Abstract
Artificial intelligence has revolutionized many sectors with unparalleled predictive capabilities supported by machine learning (ML). So far, this tool has not been able to provide the same level of development in pharmaceutical nanotechnology. This review discusses the current data science methodologies related to polymeric drug-loaded nanoparticle production from an innovative multidisciplinary perspective while considering the strictest data science practices. Several methodological and data interpretation flaws were identified by analyzing the few qualified ML studies. Most issues lie in following appropriate analysis steps, such as cross-validation, balancing data, or testing alternative models. Thus, better-planned studies following the recommended data science analysis steps along with adequate numbers of experiments would change the current landscape, allowing the exploration of the full potential of ML.
Indexed as
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.