Revolutionizing Future Periodontal Care Models: The Role of AI-Assisted Dynamic OHRQoL Assessment and Personalized Treatment Pathways in Large-Scale Digital Dental Hospital Construction

Abstract

Periodontal diseases have a considerable impact on oral health-related quality of life (OHRQoL), but existing clinical practice is based mainly on static cross-sectional assessments, which do not allow us to understand the recovery process and make appropriate treatment changes. This paper aims to provide a framework for the dynamic assessment of OHRQoL with the aid of artificial intelligence (AI) and suggest how this can be implemented in large-scale digital dental hospitals. The framework comprises three interconnected modules, a dynamic system for data acquisition based on the Oral Health Impact Profile-14 (OHIP-14) instrument, a machine learning-based trajectory prediction system to predict the recovery path for individuals in relation to a reference profile for the respective diseases, and a clinical decision support system to generate personalized pathway recommendations based on diverging observed and predicted recovery paths. The evidence of differential improvement in OHRQoL for gingivitis and periodontitis patients following Phase I therapy provides the rationale for the three-tier pathway stratification system. This system includes high response, standard, and low response pathways. A phased implementation strategy, as well as a clinical scenario, helps elucidate the rationale for the framework in a real-world setting. By shifting the paradigm of OHRQoL monitoring from an episodic administrative tool to an ongoing driver of individualized clinical decision-making, this framework represents a scalable and evidence-based approach to the achievement of precision periodontal care through a digital dental hospital.