A Theoretical Framework for Integrating Artificial Intelligence into Oral Cancer Diagnosis in Nigerian Dental Practice

Artificial intelligence tools for oral cancer diagnosis offer the potential to bridge the specialist dental oncology gap in Nigeria, and developing a theoretical framework for ethically sound and contextually appropriate AI integration in Nigerian dental practice constitutes a significant contribution at the intersection of dental oncology, AI ethics, and global health. This study developed an original Theoretical Framework for AI Integration in Oral Cancer Diagnosis for Nigerian Dental Practice. A constructive theoretical methodology was employed, combining systematic review of AI-assisted oral cancer diagnosis literature (84 publications from 2019 to 2024), ethical analysis of AI deployment in low-resource health settings, structured consultations with 30 Nigerian dental oncologists, AI specialists, ethicists, and patient representatives, and technical feasibility analysis of AI deployment options under Nigerian connectivity constraints. Systematic review confirmed that AI models achieved high diagnostic accuracy for oral mucosal lesions but were predominantly trained on non-African image datasets, creating potential performance degradation for darker oral mucosa presentations common in Nigerian patients. The original Nigerian AI Oral Cancer Diagnosis Framework proposes five components: training data requirements for Nigerian representation, validation methodology for local deployment, human-in-the-loop clinical governance, equitable access architecture, and patient data sovereignty provisions under NDPA 2023. Expert review by 24 specialists confirmed the framework's original contribution. Recommendations include a national Nigerian oral mucosal lesion image database, NAFDAC regulatory pathway for AI medical devices, and a pilot deployment at three teaching hospitals with bias monitoring protocols.

Keywords: artificial intelligence, oral cancer diagnosis, AI framework, Nigerian dental practice, oral mucosal lesion