STM Journals Peer-reviewed research from Consortium e-Learning Network

Journal of Artificial Intelligence Research & Advances Review Article

Artificial Intelligence in Healthcare for Implants and Tissue Regeneration: Advances, Challenges, and Future Directions

  1. Abdul Rahman Mechanical Engineering Department, Al-Kabir Institute of Management & Technology
  2. MD. MAQSOOD ALAM Mechanical Engineering Department, Al-Kabir Institute of Management & Technology

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

References (55)

  1. Mohajer-Bastami, A., Moin, S., Ahmad, S., Ahmed, A. R., Pouwels, S., Hajibandeh, S., ... &
  2. Exadaktylos, A. K. (2025). Artificial intelligence in healthcare: applications, challenges, and future
  3. directions. A narrative review informed by international, multidisciplinary expertise. Frontiers in
  4. Digital Health, 7, 1644041.
  5. Samah, T. A. W. I. L., & Samar, M. E. R. H. I. (2025). Investigating the Key Trends in Applying
  6. Artificial Intelligence to Health Technologies: A Scoping Review. Plos one, 20(5), e0322197.
  7. Wang, J., Bi, H., Wang, Y., Song, Y., Xu, H., Zhong, S., ... & Zhang, Q. (2025). Application of
  8. artificial intelligence in postoperative orthopedic rehabilitation: a scoping review. Frontiers in Digital
  9. Health, 7, 1746552.
  10. Bianchi, V., Giambusso, M., De Iacob, A., Chiarello, M. M., & Brisinda, G. (2024). Artificial
  11. intelligence in the diagnosis and treatment of acute appendicitis: a narrative review. Updates in
  12. Surgery, 76(3), 783-792.
  13. Bagherpour, R., Bagherpour, G., & Mohammadi, P. (2025). Application of artificial intelligence in
  14. tissue engineering. Tissue Engineering Part B: Reviews, 31(1), 31-43.
  15. Guo, J. L., Januszyk, M., & Longaker, M. T. (2023). Machine learning in tissue engineering. Tissue
  16. Engineering Part A, 29(1-2), 2-19.
  17. Huang, M., Dissanayaka, W. L., & Yiu, C. K. (2025). Artificial Intelligence Driven Innovation:
  18. Advancing Mesenchymal Stem Cell Therapies and Intelligent Biomaterials for Regenerative
  19. Medicine. Bioengineering, 12(12), 1302.
  20. Kong, M., Zeng, Y., Wu, Z., Deng, H., Zhang, B., Feng, D., ... & Wang, L. (2025). Artificial
  21. intelligence for design strategies of tissue engineering materials. Fundamental Research.
  22. Bornes, R. S., Montero, J., Correia, A. R. M., & das Neves Rosa, N. R. (2023). Use of bioinformatic
  23. strategies as a predictive tool in implant-supported oral rehabilitation: A scoping review. The Journal
  24. of Prosthetic Dentistry, 129(2), 322-e1.
  25. Shahrebabaki, K. E., Goli, M., Mohammadzadeh, M., Labbaf, S., & Shams, F. (2026). Titanium
  26. dental implants: Principles, challenges, and artificial intelligence (AI)-driven innovations. Applied
  27. Materials Today, 48, 103127.
  28. Song, P., Zhou, D., Wang, F., Li, G., Bai, L., & Su, J. (2024). Programmable biomaterials for bone
  29. regeneration. Materials Today Bio, 29, 101296.
  30. Miao, X. Y., Chen, L., Zhang, S. H., Li, J., Feng, Y., Li, X., ... & Niu, L. N. (2026). Artificial
  31. intelligence in biomaterials for oral oncology. Biomaterials, 124067.
  32. Memon, A. R., Li, J., Egger, J., & Chen, X. (2021). A review on patient-specific facial and cranial
  33. implant design using Artificial Intelligence (AI) techniques. Expert review of medical devices, 18(10),
  34. Griggs, J. A. (2024). Artificial Neural Networks for the Design Optimization of Implants. In Artificial
  35. Intelligence in Dentistry (pp. 83-96). Cham: Springer International Publishing.
  36. Macrì, M., D’Albis, V., D’Albis, G., Forte, M., Capodiferro, S., Favia, G., ... & Festa, F. (2024). The
  37. role and applications of artificial intelligence in dental implant planning: a systematic
  38. review. Bioengineering, 11(8), 778.
  39. Kumar, R., Sporn, K., Ong, J., Waisberg, E., Paladugu, P., Vaja, S., ... & Masalkhi, M. (2025).
  40. Integrating artificial intelligence in orthopedic care: advancements in bone care and future
  41. directions. Bioengineering, 12(5), 513.
  42. Martelli, A., Bellucci, D., & Cannillo, V. (2025). The role of artificial intelligence in biomaterials
  43. science: A review. Polymers, 17(19), 2668.
  44. Rokaya, D., Subedi, S., Selvido, D. I., Anh, T. H., Marya, A., Jagtap, R. D., & Al Jaghsi, A. (2026).
  45. Intelligent tools for biomaterials design. In Artificial Intelligence in Biomaterials Design and
  46. Development (pp. 45-60). Woodhead Publishing.
  47. Selvido, D. I., Rokaya, D., Jagtap, R. D., Zafar, M. S., & Tsoi, J. (2026). Artificial intelligence-based
  48. biomaterials characterization. In Artificial Intelligence in Biomaterials Design and Development (pp.
  49. 117-140). Woodhead Publishing.
  50. Basu, B., Gowtham, N. H., Xiao, Y., Kalidindi, S. R., & Leong, K. W. (2022). Biomaterialomics:
  51. Data science-driven pathways to develop fourth-generation biomaterials. Acta Biomaterialia, 143, 1-
  52. Jiang, P., Dai, Y., Hou, Y., Stein, J., Lin, S. S., Zhou, C., ... & Yang, L. (2025). Artificial
  53. intelligence‐assisted design, synthesis and analysis of smart biomaterials. BMEMat, 3(4), e70004.
  54. Pemmada, R., Gowtham, N. H., Xia, Y., Basu, B., & Thomas, V. (2023). ML and AI approaches for
  55. design of tissue scaffolds. Artificial intelligence in tissue and organ regeneration, 29-56.
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