Accepted for/Published in: JMIR Research Protocols
Date Submitted: Apr 14, 2026
Date Accepted: Jun 25, 2026
Artificial Intelligence applied to formulation design, process optimization, and quality control in 3D-printed drug products: a scoping review protocol
ABSTRACT
Background:
Three-dimensional (3D) printing is an advanced, Computer-Aided Design (CAD)-guided, layer-by-layer manufacturing technology with strong potential for patient-specific drug products. Integrating AI with pharmaceutical 3D printing can accelerate formulation development, improve process robustness and quality, and enable efficient personalization across the pre-printing, in-process, and post-printing stages. However, evidence remains dispersed across techniques, dosage forms, and AI approaches, highlighting the need for a structure map of current practice and gaps.
Objective:
To systematically map and synthesize the available scientific evidence on the application of artificial intelligence (AI) in the formulation design, process optimization, and quality control of 3D-printed drug products, identifying current approaches, methodological patterns, and existing research gaps.
Methods:
This review will follow the Joanna Briggs Institute (JBI) methodology for scoping reviews. The following databases and resources will be searched: MEDLINE/PubMed, Scopus, and Web of Science. Screening, data extraction, and data analysis will be conducted by 2 reviewers independently. Findings will be presented in summary tables and narrative synthesis.
Results:
This scoping review will map how AI is applied to formulation design, process optimization, and quality control in 3D-printed drug products, identifying key gaps and inconsistencies in methods, validation, and reporting. It will highlight limitations of the included studies and outline implications for future research and the integration of AI into pharmaceutical manufacturing.
Conclusions:
This scoping review is expected to provide a comprehensive overview of how AI is currently integrated into pharmaceutical 3D printing, revealing significant heterogeneity in methods, validation strategies, and reporting standards. By identifying key gaps and inconsistencies, the findings will support the development of more robust, standardized approaches and guide future research toward the effective and safe implementation of AI-driven technologies in pharmaceutical manufacturing. Clinical Trial: Open Science Framework (OSF) - osf.io/jbd3g
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Copyright
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