ArticlePharmaceutical research2024
The Role of Artificial Intelligence and Machine Learning in Accelerating the Discovery and Development of Nanomedicine.
Article in Pharmaceutical research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 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
21 citing papers in PubMed.
- Review
- Artificial Intelligence-Driven Nanomedicine: From Drug Formulation and Nanocarrier Design to Clinical Translation.Pharmaceutics · 2026Review
- Multifunctional Nano-Contrast Agent Carriers: From Traditional Platforms to Next-Generation Theranostic Applications in Molecular Imaging.Biomedicines · 2026Review
- Engineering smart polymeric lipid nanoparticles for breast cancer: AI-guided formulation design, biological barriers, and translational constraints.Journal of nanobiotechnology · 2026Review
- Artificial Intelligence in Nanopharmaceutical Development: From Predictive Design to Clinical Translation.Pharmaceutics · 2026Review
- Harnessing Machine Learning for Accelerated Drug Discovery: Opportunities and Unmet Challenges.Pharmaceuticals (Basel, Switzerland) · 2026Review
- The landscape of nanomedical clinical trials.Nano today · 2026Article
- Implementing QbD for Nano-Pharmaceuticals and Complex Formulations to Achieve Predictable and High-Quality Outcomes.AAPS PharmSciTech · 2026Review
- Application and challenges of smart responsive multifunctional nanoplatforms in integrated diagnosis and treatment of bladder cancer and overcoming therapeutic resistance.Frontiers in oncology · 2026Review
- Toward oral nanomaterial-based drug delivery systems for hepatocellular carcinoma therapy: evidence mapping, route-specific validation, and translational challenges.Frontiers in pharmacology · 2026Review
- Advances in Biomimetic Cell Membrane Nanoplatforms for Renal-Targeted Theranostics: From Pathophysiological Basis to Membrane-Stratified Design.International journal of nanomedicine · 2026Review
- Nanomedicine against antimicrobial resistance: mechanistic insights and next-generation therapeutic potential.Frontiers in chemistry · 2026Review
- Recent Progress in Selenium Nanomedicines for Ocular Diseases.International journal of nanomedicine · 2026Review
- Nanomaterials in gene therapy and genome editing: challenges and emerging directions.Journal of nanobiotechnology · 2025Review
- Metabolism-based artificial organelles: From precise construction to smart theranostics.Materials today. Bio · 2025Review
- Advances in the application of lipid nanocapsules and nanostructured carriers in the treatment of lung cancer.Nanomedicine (London, England) · 2025Review
- Precision nanomaterials in colorectal cancer: advancing photodynamic and photothermal therapy.RSC advances · 2025Review
- Nanoparticle Therapeutics in Clinical Perspective: Classification, Marketed Products, and Regulatory Landscape.Small (Weinheim an der Bergstrasse, Germany) · 2025Review
- Nanotechnology-Driven Drug Delivery Systems for Lung Cancer: Computational Advances and Clinical Perspectives.Thoracic cancer · 2025Review
- Artificial Intelligence Advancements in Oncology: A Review of Current Trends and Future Directions.Biomedicines · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
The unique potential of nanomedicine to address challenging health issues is rapidly advancing the field, leading to the generation of more effective products. However, these complex systems often pose several challenges with respect to their design for specific functionality, scalable manufacturing, characterization, quality control, and clinical translation. In this regard, the application of artificial intelligence (AI) and machine learning (ML) approaches can enable faster and more accurate data assessment, identifying trends and predicting outcomes, leading to efficient nanomedicine product development. This perspective paper discusses the potential of AI and ML in nanomedicine product development with a focus on their applications in discovery, assessment, manufacturing, and clinical trials. The potential limitations of AI and ML approaches in nanomedicine product development are also covered.
Indexed as
Identifiers
39623144What 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.