ReviewFrontiers in digital health2022
Nanomedicine Ex Machina: Between Model-Informed Development and Artificial Intelligence.
Review in Frontiers in digital health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 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
11 citing papers in PubMed.
- Nanomaterials reshaping cancer radiotherapy: Radiosensitization mechanisms, delivery and theranostic platforms, multimodal synergy, and clinical translation strategies.Materials today. Bio · 2026Review
- Integration of Machine Learning With PBPK and QSAR Modeling Approaches to Facilitate Drug Discovery and Development.CPT: pharmacometrics & systems pharmacology · 2026Review
- Nanomaterials in Drug Delivery: Leveraging Artificial Intelligence and Big Data for Predictive Design.International journal of molecular sciences · 2025Review
- Utilizing machine learning to predict MRI signal outputs from iron oxide nanoparticles through the PSLG algorithm.Scientific reports · 2025Article
- Multimodal AI in Biomedicine: Pioneering the Future of Biomaterials, Diagnostics, and Personalized Healthcare.Nanomaterials (Basel, Switzerland) · 2025Review
- Artificial Intelligence-Driven Innovations in Pharmaceutical Development and Drug Delivery Systems.Current topics in medicinal chemistry · 2025Review
- The Role of Artificial Intelligence and Machine Learning in Accelerating the Discovery and Development of Nanomedicine.Pharmaceutical research · 2024Article
- Advancements in Nanoparticle Characterization.Methods in molecular biology (Clifton, N.J.) · 2024Article
- Artificial Intelligence in Pharmaceutical Technology and Drug Delivery Design.Pharmaceutics · 2023Review
- Frontiers in hepatic drug delivery-grand challenges.Frontiers in drug delivery · 2023Article
- Nanomaterials in diagnostics, imaging and delivery: Applications from COVID-19 to cancer.MRS communications · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Today, a growing number of computational aids and simulations are shaping model-informed drug development. Artificial intelligence, a family of self-learning algorithms, is only the latest emerging trend applied by academic researchers and the pharmaceutical industry. Nanomedicine successfully conquered several niche markets and offers a wide variety of innovative drug delivery strategies. Still, only a small number of patients benefit from these advanced treatments, and the number of data sources is very limited. As a consequence, "big data" approaches are not always feasible and smart combinations of human and artificial intelligence define the research landscape. These methodologies will potentially transform the future of nanomedicine and define new challenges and limitations of machine learning in their development. In our review, we present an overview of modeling and artificial intelligence applications in the development and manufacture of nanomedicines. Also, we elucidate the role of each method as a facilitator of breakthroughs and highlight important limitations.
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.