In the rapidly evolving landscape of biomedical technology, a promising intersection of artificial intelligence (AI), machine learning (ML), and hydrogel microneedles (HMNs) is emerging, offering a glimpse into the future of drug delivery, diagnostics, and even agricultural sensing. A recent review published in *Micro*, led by Jannah Urifa from the Faculty of Mechanical and Aerospace Engineering at the Bandung Institute of Technology, Indonesia, sheds light on the transformative potential of AI/ML in advancing HMN technologies.
Hydrogel microneedles are tiny, non-invasive devices designed to seamlessly integrate with the human body for various medical applications. However, their development has been hindered by complex challenges related to material composition, structural geometry, manufacturing precision, and performance enhancement. The integration of AI/ML into HMN research is still in its infancy, with data scattered across disparate studies and fields. Urifa’s review aims to consolidate these fragmented insights, offering a comprehensive synthesis of interdisciplinary research categorized into five key areas: material and microneedle design, diagnostics and therapy, drug delivery, drug development, and health and agricultural sensing.
The review highlights typical AI methods, integration approaches, proven advantages, and ongoing challenges in each domain. For instance, AI can enhance material discovery and structural design through predictive modeling, adaptive control, and process optimization. “AI/ML can significantly enhance HMN development by addressing design and fabrication constraints,” Urifa explains. “By synchronizing these abilities with clinical and commercial translation requirements, AI/ML can act as key facilitators in converting HMNs from research ideas into scalable, practical biomedical solutions.”
The implications for the agricultural sector are particularly intriguing. HMNs equipped with AI-driven sensing capabilities could revolutionize crop monitoring and disease detection. Imagine microneedles embedded in plants, continuously collecting data on soil health, nutrient levels, and pest infestations, and transmitting this information to farmers in real-time. This technology could lead to more precise and efficient agricultural practices, ultimately increasing crop yields and reducing environmental impact.
The review suggests a systematic five-stage developmental pathway for HMNs, covering material discovery, structural design, manufacturing, biomedical performance, and advanced AI integration. This pathway is intended to expedite the transition of HMNs from research ideas to clinically and commercially practical systems. The findings indicate that AI/ML can play a pivotal role in overcoming the current limitations of HMN development, paving the way for innovative applications in both healthcare and agriculture.
As we stand on the brink of this technological revolution, the integration of AI/ML into HMN research holds immense promise. The work of Urifa and her colleagues not only highlights the current state of the field but also offers a roadmap for future developments. By harnessing the power of AI/ML, we can unlock new possibilities in drug delivery, diagnostics, and agricultural sensing, ultimately improving lives and enhancing sustainability. The journey from lab to market is complex, but with the right tools and insights, the future of HMNs looks brighter than ever.