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Abdul Rahman,
MD. MAQSOOD ALAM,
- Lecturer, Mechanical Engineering Department, Al-Kabir Institute of Management & Technology, Jharkhand, India
- Professor, Mechanical Engineering Department, Al-Kabir Institute of Management & Technology, Jharkhand, India
Abstract
Artificial intelligence (AI) has been a revolutionary influence in contemporary healthcare, especially in implant design, biomaterials research, and tissue regeneration. In regenerative medicine, AI facilitates predictive modeling, optimization, and decision-making via the analysis of intricate biological, material, and clinical information. This study analyzes current research on AI applications in implant technologies and tissue regeneration, specifically addressing scaffold engineering, biomaterial characterisation, stem cell and gene treatments, smart biomaterials, and implant planning. Focus is directed towards machine learning, deep learning, data fusion methodologies, computational modeling, and AI-enhanced imaging and bioprinting technologies in regenerative medicine and implant sciences. Research indicates that AI improves scaffold design, forecasts cellular activity and differentiation results, and refines biomaterial–cell interactions, expediting the shift from empirical testing to data-driven customization. In implantology, artificial intelligence enhances diagnosis, treatment planning, and personalized implant design by integrating imaging, CAD/CAM technologies, and predictive algorithms. AI-enhanced intelligent biomaterials and computational instruments facilitate the development of stimuli-responsive systems for tissue regeneration and regulated medicinal administration. Nonetheless, ongoing challenges—such as restricted data availability, model interpretability, ethical dilemmas, technical integration obstacles, and inadequate clinical validation—persistently hinder large-scale clinical translation. The integration of AI with implant technologies and regenerative medicine signifies a rapidly advancing domain with considerable promise to enhance precision-oriented, patient-specific therapy. Realizing this promise requires high-quality standardized datasets, interpretable AI frameworks, multidisciplinary cooperation, and strong regulatory channels to guarantee safe, effective, and scalable clinical deployment.
Keywords: Artificial Intelligence; Tissue Regeneration; Biomaterials; Implants; Machine Learning; 3D Bioprinting
References
[1] Mohajer-Bastami, A., Moin, S., Ahmad, S., Ahmed, A. R., Pouwels, S., Hajibandeh, S., … &
Exadaktylos, A. K. (2025). Artificial intelligence in healthcare: applications, challenges, and future
directions. A narrative review informed by international, multidisciplinary expertise. Frontiers in
Digital Health, 7, 1644041.
[2] Samah, T. A. W. I. L., & Samar, M. E. R. H. I. (2025). Investigating the Key Trends in Applying
Artificial Intelligence to Health Technologies: A Scoping Review. Plos one, 20(5), e0322197.
[3] Wang, J., Bi, H., Wang, Y., Song, Y., Xu, H., Zhong, S., … & Zhang, Q. (2025). Application of
artificial intelligence in postoperative orthopedic rehabilitation: a scoping review. Frontiers in Digital
Health, 7, 1746552.
[4] Bianchi, V., Giambusso, M., De Iacob, A., Chiarello, M. M., & Brisinda, G. (2024). Artificial
intelligence in the diagnosis and treatment of acute appendicitis: a narrative review. Updates in
Surgery, 76(3), 783-792.
[5] Bagherpour, R., Bagherpour, G., & Mohammadi, P. (2025). Application of artificial intelligence in
tissue engineering. Tissue Engineering Part B: Reviews, 31(1), 31-43.
[6] Guo, J. L., Januszyk, M., & Longaker, M. T. (2023). Machine learning in tissue engineering. Tissue
Engineering Part A, 29(1-2), 2-19.
[7] Huang, M., Dissanayaka, W. L., & Yiu, C. K. (2025). Artificial Intelligence Driven Innovation:
Advancing Mesenchymal Stem Cell Therapies and Intelligent Biomaterials for Regenerative
Medicine. Bioengineering, 12(12), 1302.
[8] Kong, M., Zeng, Y., Wu, Z., Deng, H., Zhang, B., Feng, D., … & Wang, L. (2025). Artificial
intelligence for design strategies of tissue engineering materials. Fundamental Research.
[9] Bornes, R. S., Montero, J., Correia, A. R. M., & das Neves Rosa, N. R. (2023). Use of bioinformatic
strategies as a predictive tool in implant-supported oral rehabilitation: A scoping review. The Journal
of Prosthetic Dentistry, 129(2), 322-e1.
[10] Shahrebabaki, K. E., Goli, M., Mohammadzadeh, M., Labbaf, S., & Shams, F. (2026). Titanium
dental implants: Principles, challenges, and artificial intelligence (AI)-driven innovations. Applied
Materials Today, 48, 103127.
[11] Song, P., Zhou, D., Wang, F., Li, G., Bai, L., & Su, J. (2024). Programmable biomaterials for bone
regeneration. Materials Today Bio, 29, 101296.
[12] Miao, X. Y., Chen, L., Zhang, S. H., Li, J., Feng, Y., Li, X., … & Niu, L. N. (2026). Artificial
intelligence in biomaterials for oral oncology. Biomaterials, 124067.
[13] Memon, A. R., Li, J., Egger, J., & Chen, X. (2021). A review on patient-specific facial and cranial
implant design using Artificial Intelligence (AI) techniques. Expert review of medical devices, 18(10),
985-994.
[14] Griggs, J. A. (2024). Artificial Neural Networks for the Design Optimization of Implants. In Artificial
Intelligence in Dentistry (pp. 83-96). Cham: Springer International Publishing.
[15] Macrì, M., D’Albis, V., D’Albis, G., Forte, M., Capodiferro, S., Favia, G., … & Festa, F. (2024). The
role and applications of artificial intelligence in dental implant planning: a systematic
review. Bioengineering, 11(8), 778.
[16] Kumar, R., Sporn, K., Ong, J., Waisberg, E., Paladugu, P., Vaja, S., … & Masalkhi, M. (2025).
Integrating artificial intelligence in orthopedic care: advancements in bone care and future
directions. Bioengineering, 12(5), 513.
[17] Martelli, A., Bellucci, D., & Cannillo, V. (2025). The role of artificial intelligence in biomaterials
science: A review. Polymers, 17(19), 2668.
[18] Rokaya, D., Subedi, S., Selvido, D. I., Anh, T. H., Marya, A., Jagtap, R. D., & Al Jaghsi, A. (2026).
Intelligent tools for biomaterials design. In Artificial Intelligence in Biomaterials Design and
Development (pp. 45-60). Woodhead Publishing.
[19] Selvido, D. I., Rokaya, D., Jagtap, R. D., Zafar, M. S., & Tsoi, J. (2026). Artificial intelligence-based
biomaterials characterization. In Artificial Intelligence in Biomaterials Design and Development (pp.
117-140). Woodhead Publishing.
[20] Basu, B., Gowtham, N. H., Xiao, Y., Kalidindi, S. R., & Leong, K. W. (2022). Biomaterialomics:
Data science-driven pathways to develop fourth-generation biomaterials. Acta Biomaterialia, 143, 1-
25.
[21] Jiang, P., Dai, Y., Hou, Y., Stein, J., Lin, S. S., Zhou, C., … & Yang, L. (2025). Artificial
intelligence‐assisted design, synthesis and analysis of smart biomaterials. BMEMat, 3(4), e70004.
[22] Pemmada, R., Gowtham, N. H., Xia, Y., Basu, B., & Thomas, V. (2023). ML and AI approaches for
design of tissue scaffolds. Artificial intelligence in tissue and organ regeneration, 29-56.
Journal of Artificial Intelligence Research & Advances
| Volume | 13 | |
| 03 | ||
| Received | 18/04/2026 | |
| Accepted | 04/08/2026 | |
| Published | 30/09/2026 | |
| Publication Time | 165 Days |