Artificial intelligence in biomaterials for oral oncology

Clinical Snapshot

Key Findings

• P Value: Not reported at review level

• Effect Size: Not reported — no pooled quantitative effect sizes are presented; individual study findings are described narratively

• Primary Outcome: Narrative synthesis of AI-enhanced biomaterial applications in oral cancer and OPMDs across three domains: (1) early detection via AI-assisted biosensing and radiomic analysis, (2) targeted drug delivery optimisation using machine learning-guided nanoparticle and hydrogel design, and (3) computer-aided scaffold fabrication for maxillofacial reconstruction

• Nnt Or Sensitivity: Not calculable from this review — individual diagnostic accuracy metrics (sensitivity/specificity of AI biosensing or radiomic models) from primary studies are referenced narratively but not pooled or formally synthesised

• Confidence Interval: Not applicable — no meta-analytic synthesis performed

Clinical Application

Clinical translation remains early-stage and largely preclinical. Key feasibility barriers include: (1) requirement for large, high-quality annotated datasets for AI model training; (2) regulatory approval pathways for AI-integrated medical devices and novel biomaterials; (3) manufacturing scalability and cost of personalised nanoparticle or scaffold systems; (4) need for prospective clinical validation in diverse patient populations; (5) integration with existing clinical workflows in oral and maxillofacial surgery, radiation oncology, and prosthodontics. In Australia, oral cancer represents approximately 4% of all cancers, with OSCC being the predominant subtype. The TGA regulates both therapeutic goods (including novel biomaterials and drug delivery systems) and software as a medical device (SaMD), the latter under an evolving regulatory framework aligned with international standards (IMDRF). AI-based diagnostic tools for oral cancer would require TGA SaMD classification and conformity assessment before clinical deployment. No AI-integrated biomaterial for oral oncology is currently listed on the Australian Register of Therapeutic Goods (ARTG). The PBS does not currently subsidise any AI-guided biomaterial therapy for oral cancer. RACGP and DCNA guidelines emphasise early detection of OPMDs through clinical oral examination; AI-enhanced biosensing tools, if validated, could complement but not replace current screening pathways. Australian oral and maxillofacial surgeons and prosthodontists would be the primary clinical adopters of AI-guided scaffold fabrication technologies, which aligns with existing CAD/CAM prosthodontic infrastructure in tertiary centres. Adults with confirmed or suspected oral squamous cell carcinoma (OSCC), oral potentially malignant disorders (leukoplakia, erythroplakia, oral submucous fibrosis), or those requiring postoperative maxillofacial reconstruction following oncological resection. Applicability is currently limited to research and early clinical trial settings.

Abstract

Oral cancer and oral potentially malignant disorders (OPMDs) remain a significant challenge in diagnosis and therapy, primarily due to inherent limitations in early detection, targeted treatment, and postoperative rehabilitation. Conventional diagnostic and therapeutic modalities often lack sufficient sensitivity, specificity, and effectiveness in restoring oral function. Biomaterials including nanoparticles, hydrogels, and scaffolds, offer versatile solutions by virtue of their tuneable properties, biocompatibility, and versatility in drug delivery and tissue engineering. However, their clinical translation is limited by the need for personalisation and lingering efficacy concerns. Artificial intelligence (AI) has emerged as a transformative approach to advance the design, optimisation, and application of biomaterials in oral oncology. By integrating machine learning (ML) and data-driven modelling, AI enhances diagnostic accuracy through biosensing and radiomic analysis, guides the rational design of drug carriers and dosing regimens, and facilitates computer-aided scaffold fabrication for maxillofacial reconstruction. This review summarises recent advances at the intersection of AI and biomaterials in the context of oral cancer and OPMDs, highlighting innovations in early detection, targeted therapy, and postoperative repair. It also discusses current barriers, including data quality, model generalizability, and regulatory oversight, and outlines future directions for interdisciplinary research. When properly integrated, AI-enabled biomaterials hold considerable potential to deliver more precise, efficient, and patient-tailored solutions for oral cancer management.

References

1. 1.Miao, X.-Y., Chen, L., Zhang, S.-H., Li, J., Feng, Y., Li, X., Wei, J.-H., Xue, Y., Bai, S.-Z., Tay, F. R., & Niu, L.-N. (2026). Artificial intelligence in biomaterials for oral oncology. Biomaterials. Advance online publication. https://doi.org/10.1016/j.biomaterials.2026.124067