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REVIEW
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Building the next frontier: Artificial intelligence in 3D-printed medicines

Rittin Abraham Kurien1,2,3,4 ,  Gokul Kannan2,5,6 ,  Kasitpong Thanawut7 ,  Supakij Suttiruengwong2 ,  Pornsak Sriamornsak1,8*
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1 Department of Industrial Pharmacy, Faculty of Pharmacy, Silpakorn University, Nakhon Pathom, Thailand
2 Sustainable Materials Laboratory, Department of Materials Science and Engineering, Faculty of Engineering and Industrial Technology, Silpakorn University, Nakhon Pathom, Thailand
3 Department of Mechanical Engineering, Saintgits College of Engineering (Autonomous), Kottayam, Kerala, India
4 School of Mechanical Sciences and Technology, APJ Abdul Kalam Technological University, Thiruvananthapuram, Kerala, India
5 Centre for Material Science, Easwari Engineering College, Chennai, Tamil Nadu, India
6 Center for Research, SRM TRP Engineering College, Tiruchirappalli, Tamil Nadu, India
7 Department of Industrial Pharmacy, College of Pharmacy, Rangsit University, Pathum Thani, Thailand
8 Academy of Science, The Royal Society of Thailand, Bangkok, Thailand
BMT, 00043 https://doi.org/10.12336/bmt.25.00043
Submitted: 23 May 2025 | Revised: 8 July 2025 | Accepted: 11 July 2025 | Published: 14 August 2025
Copyright © 2025 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution–NonCommercial–ShareAlike 4.0 License.
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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. 

Keywords
Artificial intelligence
3D printing
Machine learning
Neural networks
Bioprinting
Industry 4.0
Funding
This work was financially supported by Thailand Science Research and Innovation under the National Science, Research and Innovation Fund, Fiscal Year 2568, and by the postdoctoral fellowship program at Silpakorn University (R.A.K.).
Conflict of interest
The authors declare no conflicts of interest.
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