ReviewBiomaterials translational2026
Building the next frontier: Artificial intelligence in 3D-printed medicines.
Review in Biomaterials translational, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
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
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence (AI) and 3D printing are transforming pharmaceutical manufacturing by enabling the production of personalized medications. AI supports real-time decision-making in diagnostics and robotics, although its application in pharmaceutical research remains at an early stage. 3D printing, particularly additive manufacturing, provides precise control over drug formulation, allowing the design of patient-specific dosage forms with tailored release profiles. Machine learning and deep neural networks are used to predict formulation parameters, optimize processing conditions, and support the design of innovative drug delivery geometries. Technological platforms such as cloud computing and blockchain enhance data security, transparency, and scalability. Printable materials-including thermoplastic polymers, hydrogels, and bioinks-demonstrate utility in AI-assisted manufacturing systems. The integration of AI, smart materials, and 3D printing advances intelligent drug production technologies aligned with Industry 4.0 principles. Key considerations include regulatory compliance, data reliability, ethical implications, and pathways for clinical translation. Clinical medicine is rapidly advancing through the adoption of 3D printing and AI, enabling personalized prosthetics, accurate surgical planning, and bioprinted tissues. AI-driven segmentation and optimization enhance the accuracy and efficiency of 3D-printed anatomical models for pre-operative preparations and medical training. Cardiology, oncology, and orthopedics are increasingly adopting these technologies to improve patient outcomes and clinical workflows. Future directions include broader adoption across specialties, bioprinting for regenerative health care, and AI-optimized systems for targeted drug delivery. This review addresses the current challenges and limitations of AI and 3D-printed medicines, pharmaceutical manufacturing, case studies, ethical considerations, and future perspectives.
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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.