ReviewPharmaceutics2025
Use of Computational Intelligence in Customizing Drug Release from 3D-Printed Products: A Comprehensive Review.
Review in Pharmaceutics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 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
2 citing papers in PubMed.
- Integrating Artificial Intelligence with Emerging Pharmaceutical Technologies: Current Progress, Clinical Translation, and Future Challenges.International journal of molecular sciences · 2026Review
- Building the next frontier: Artificial intelligence in 3D-printed medicines.Biomaterials translational · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Computational intelligence (CI) mimics human intelligence by expanding the capabilities of machines in data analysis, pattern recognition, and making informed decisions. CI has shown promising contributions to advancements in drug discovery, formulation, and manufacturing. Its ability to analyze vast amounts of patient data and optimize drug formulations by predicting pharmacokinetic and pharmacodynamic responses makes it a very useful platform for personalized medicine. The integration of CI with 3D printing further strengthens this potential, as 3D printing enables the fabrication of personalized medicines with precise doses, controlled-release profiles, and complex formulations. Furthermore, the automated and digital capabilities of 3D printing make it suitable for integration with CI. CI has proven useful in predicting material printability, optimizing drug release rates, designing complex structures, ensuring quality control, and improving manufacturing processes in 3D printing. In the context of customizing drug release from 3D-printed products, CI techniques have been applied to predict drug release from input variables and to design geometries that achieve the desired release profile. This review explores the role of CI in customizing drug release from 3D-printed formulations. It provides overview of limitations of 3D printing; how CI can overcome these challenges, and its potential in customizing drug release; a comparison of CI with other methods of optimization; and real-world examples of CI integration in 3D printing.
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.