Document Type : Review Paper
Authors
1
Department of Dentistry, Unciano Colleges and General Hospital, Manila, Philippines.
2
Department of Dentistry, School of Dentistry, Tehran University of Medical Sciences, Tehran,iran
3
department of Oral and Maxillofacial Surgery, faculty of dentistry,Kerman university of medical sciences, Kerman, iran
4
General Dentist, Faculty of Dentistry, Kermanshah University of Medical Sciences, Kermanshah, Iran
5
department of periodontics, faculty of dentistry, Isfahan university of medical sciences, Isfahan, Iran
6
Department of Oral and Maxillofacial Surgery, School of Dentistry, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran
7
Department of oral and maxillofacial radiology, faculty of dentistry ,Zanjan university of medical sciences ,Zanjan ,Iran
10.22034/nmrj.2026.2107982.1892
Abstract
Maxillofacial trauma can cause complex injuries involving facial bone, teeth, periodontal tissues, and oral soft tissues, often requiring accurate diagnosis, individualized treatment planning, and long-term regenerative management. Conventional imaging and reconstructive approaches may be limited by observer variability, incomplete risk prediction, and insufficient personalization. This review examines the integration of artificial intelligence (AI) and nanomedicine as a precision-regenerative framework for maxillofacial trauma care. AI-based analysis of radiographic, CT, CBCT, MRI, clinical, and biological data can support automated injury detection, defect characterization, risk stratification, virtual surgical planning, and longitudinal healing assessment. In parallel, nano-enabled biomaterials, nanocarriers, bioactive scaffolds, and controlled-release systems may improve local infection control, inflammation modulation, bone regeneration, and regeneration of periodontal and oral tissues. The convergence of AI with nanomedicine may facilitate the design of patient-specific implants, scaffolds, and therapeutic delivery systems based on individual anatomical and biological characteristics. However, successful translation requires robust external validation, standardized datasets and protocols, nanomaterial safety assessment, scalable manufacturing, regulatory clarity, and ethical safeguards related to privacy and algorithmic bias. AI-integrated nanomedicine therefore represents a promising pathway from imaging-based diagnosis toward predictive, personalized, and adaptive regenerative care in maxillofacial trauma.
Keywords
Subjects
