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The issues and trends in healthcare delivery are constantly changing. The COVID-19 pandemic is one recent issue that wreaked havoc on healthcare systems, causing a shortage of healthcare workers, high demand for medicines and supplies, and increased medical expenditure due to a lack of insurance. Other issues include rising healthcare costs and care fragmentation.
Cost Containment
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Author Spotlight: Advancing CBCT and Digital Dental Image Integration with AI-Assisted Digitization

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Advancing biomaterial research with artificial intelligence.

Jun Chen Ng¹, Pauline Shan Qing Yeoh², Farina Muhamad²

¹Department of Biomedical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur, Malaysia; School of Materials Science and Engineering, Peking University, Beijing, China.

Biomaterials Advances

|October 11, 2025

Summary

Artificial intelligence (AI) accelerates biomaterial innovation by optimizing development and overcoming fabrication challenges. This review explores AI applications, methodologies, and limitations in advancing healthcare biomaterials.

Keywords:

Artificial intelligence Ceramic biomaterial Composite biomaterial Explainable artificial intelligence Machine learning Metallic biomaterial Polymer biomaterial

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Area of Science:

Biomaterial Science and Engineering
Computational Materials Science
Medical Device Development

Background:

Biomaterials are critical in healthcare, especially for implants, necessitating the prevention of adverse effects.
Traditional biomaterial development is time-consuming, labor-intensive, and costly.
Artificial intelligence (AI) offers a transformative approach to accelerate biomaterial research and innovation.

Purpose of the Study:

To provide a comprehensive review of AI applications in biomaterial research.
To examine the role of Machine Learning (ML) and Deep Learning (DL) in various biomaterial categories.
To discuss AI methodologies and their impact on biomaterial design and characterization.

Main Methods:

Review of existing literature on AI applications in biomaterials.
Categorization of AI methodologies (supervised, unsupervised, semi-supervised, reinforcement learning).
Analysis of AI's role in forward and inverse design problems for biomaterials.

Main Results:

AI, including ML and DL, significantly enhances the performance, efficiency, and scalability of biomaterial development.
AI addresses challenges in fabrication and characterization across polymeric, metallic, ceramic, and composite biomaterials.
Key AI limitations like interpretability and data quality are identified, with emerging solutions like Explainable AI (XAI) discussed.

Conclusions:

AI is a powerful tool revolutionizing biomaterial research and development.
Explainable AI methods (SHAP, LIME) are crucial for addressing AI limitations in biomaterial applications.
The integration of AI promises significant advancements in creating next-generation biomaterials for healthcare.