The Integration of Generative AI and Multimodal Models for Diagnosis and Customized Design of Biomaterials
A special issue of Bioengineering (ISSN 2306-5354). This special issue belongs to the section "Biomedical Engineering and Biomaterials".
Deadline for manuscript submissions: 31 March 2027 | Viewed by 5
Special Issue Editors
Interests: multiscale modeling; molecular dynamics; nano mechanics; biomechanics; bioinspired material design; mycelium; mycelium-based composites; sustainable composite; building envelope; machine learning and artificial intelligence
Interests: biomaterials; bone damage mechanisms; biomimetic composites; composites; hydrogen embrittlement
Special Issue Information
Dear Colleagues,
The rapid emergence of generative artificial intelligence (AI) and multimodal learning is transforming how biomaterials are conceived, analyzed, and translated into real-world applications. Previously, biomaterial development has relied heavily on iterative experimentation and empirical optimization. The integration of physics-based modeling, limited diagnostic or experimental data, and AI tools now enables a fundamentally different paradigm. By embedding physical constraints and mechanistic understanding into data-driven frameworks, researchers can augment sparse datasets, uncover robust structure–function relationships, and construct predictive models that guide inverse design strategies. This convergence creates new opportunities for developing customized biomaterials tailored to specific mechanical, regional, or clinical requirements, while ensuring structural robustness and long-term functional durability.
This Special Issue seeks contributions that advance this integrated workflow, from the use of physical models or diagnostic data to inform generative systems to data augmentation strategies that expand accessible design spaces and ultimately to predictive frameworks supporting customized, performance-driven solutions. We welcome studies spanning computational modeling; material synthesis; advanced manufacturing, including biofabrication; and characterization, as well as translational efforts in biomedical device and product development. Particular interest lies in work that bridges modeling with fabrication or validation, demonstrating how AI-enabled methodologies can accelerate discovery while maintaining mechanistic rigor and practical relevance. We invite original research articles and reviews that help define the next generation of intelligent, adaptive, customizable biomaterials.
Dr. Zhao Qin
Prof. Laura Vergani
Dr. Federica Buccino
Guest Editors
Manuscript Submission Information
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Keywords
- generative artificial intelligence
- multimodal learning
- physics-informed modeling
- inverse design
- structure–function relationships
- biomaterial design
- advanced manufacturing
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