AccScience Publishing / IJB / Online First / DOI: 10.36922/IJB025350349
REVIEW ARTICLE

Artificial intelligence-augmented bioprinting systems: Data-driven optimization and future applications in pharmacological research

Yangyang Wang1 Haotian Bai1 Yufei Ren1 Yanning Yang1 Jihan Wang2*
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1 Department of Automation, School of Physics and Electronic Information, Yan’an University, Yan’an, Shaanxi, China
2 Department of Basic Medicine, Yan’an Medical College, Yan’an University, Yan’an, Shaanxi, China
Received: 25 August 2025 | Accepted: 30 October 2025 | Published online: 6 October 2025
© 2025 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International License ( https://creativecommons.org/licenses/by/4.0/ )
Abstract

The high attrition rate of drug candidates in clinical trials is often attributed to the use of conventional two-dimensional cell cultures and animal models that fail to accurately recapitulate human physiology. Three-dimensional (3D) bioprinting has emerged as a transformative technology for creating sophisticated, patient-relevant tissue models for drug screening and toxicity assessment. Concurrently, machine learning (ML) offers a powerful paradigm for extracting insights from complex, multi-modal data and optimizing intricate processes. This review presents a comprehensive and critical overview of the convergence of 3D bioprinting and ML, with a focus on their integrated applications in drug development. We critically and comprehensively analyze the various data types generated throughout the bioprinting workflow, from process parameters and material properties to biological and “omics” data. We then discuss the application of diverse ML approaches, from statistical methods to deep learning, for optimizing bioprinting processes and enhancing the predictive accuracy of drug screening. By including specific quantitative outcomes and comparative analyses from recent studies, we provide an evidence-based perspective on the state of the field and highlight its potential to accelerate the drug discovery pipeline.

Graphical abstract
Keywords
3D bioprinting
Deep learning
Drug discovery
Machine learning
Predictive modeling
Funding
This work was supported by the National Natural Science Foundation of China (No. 82560411) and the Research Project of Yan’an University (No. YAU202512552).
Conflict of interest
The authors declare that they have no conflicts of interest.
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International Journal of Bioprinting, Electronic ISSN: 2424-8002 Print ISSN: 2424-7723, Published by AccScience Publishing