Computational Innovations Within Regenerative Medicine: AI, Biomaterials, and Molecular Engineering in BME

Abstract

Recent breakthroughs in molecular engineering and computational biology are redefining the landscape of modern healthcare, diagnostics, and therapeutic innovation. Despite significant advancements, challenges remain in integrating artificial intelligence (AI), machine learning, and bioinformatics to accelerate translational outcomes in regenerative medicine and biomedical research. This study addresses this gap by investigating the convergence of AI-driven technologies with regenerative medicine, biomaterials, and tissue engineering. Key developments—such as organ-on-a-chip platforms, AI-assisted bioprinting, and computational models for tissue regeneration—are analyzed for their clinical and biomedical relevance. Furthermore, the role of predictive modeling, biomedical data analytics, and AI-guided drug discovery is explored in areas such as immune engineering, precision medicine, and gene editing. Adopting a computational and case study-based approach, the research highlights how data-driven methodologies are reshaping biologics, drug screening, and personalized therapies. The findings emphasize the transformative potential of intelligent systems in bridging regenerative medicine with biomedical informatics, ultimately aiming to accelerate therapeutic development and precision care. Received: 19 March 2025 | Revised: 8 September 2025 | Accepted: 30 November 2025 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement The various original data sources used in this study are not all publicly available, because they contain various types of private information. The available platform provided data sources that support the exploration findings, and information from the research investigations is referenced where appropriate. The data that support the findings of this study are openly available in Physionet at https://physionet.org/content/. Author Contribution Statement Zarif Bin Akhtar: Conceptualization, Methodology, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Supervision, Project administration. Ahmed Tajbiul Rawol: Software, Visualization.

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