Advancements in microneedle fabrication techniques: artificial intelligence assisted 3D-printing technology

Area of Science:

• Biomedical Engineering
• Materials Science
• Artificial Intelligence

Background:

• Microneedles (MNs) offer a painless alternative to injections for transdermal drug delivery and biosensing.
• Three-dimensional (3D) printing is a key fabrication technique for producing customizable MNs.
• Integrating Artificial Intelligence (AI) with 3D printing offers advanced control and automation in MN fabrication.

Purpose of the Study:

• To review the role of AI in the 3D printing of microneedle-based devices.
• To discuss AI applications in predicting drug release, biomarker levels, and quality control.
• To explore the potential of AI and IoT for autonomous MN manufacturing.

Main Methods:

• Review of AI applications in 3D printed microneedle fabrication.
• Discussion of machine learning (ML) categories: supervised, semi-supervised, unsupervised, and reinforced learning.
• Exploration of AI integration with 3D printing for quality control and predictive analysis.

Main Results:

• AI significantly enhances the precision and quality control of 3D printed microneedles.
• AI tools can predict drug release patterns and biomarker levels, improving device efficacy.
• Autonomous 3D printing of microneedles is achievable through integrated AI and IoT systems.

Conclusions:

• AI is crucial for advancing 3D printed microneedle technology for drug delivery and biosensing.
• AI-driven quality control and predictive capabilities pave the way for mass production.
• Future directions include further integration of AI for autonomous and intelligent microneedle systems.