Toward Precision Oral Medicine in Pemphigus Vulgaris: A Conceptual AI Framework for Rituximab Response Prediction

Background/Objectives: Rituximab has improved outcomes in pemphigus vulgaris (PV), but treatment response remains heterogeneous and no clinically validated biomarker currently supports reliable individual-level prediction. Oral lesions frequently represent the earliest manifestation of PV and contribute substantially to disease burden and longitudinal assessment. This perspective aimed to synthesize candidate biomarkers and organize them within a conceptual, task-specific artificial intelligence (AI) framework for future rituximab outcome modeling. Methods: A structured literature search and narrative synthesis evaluated genetic and pharmacogenomic, immunological and serological, cellular, clinical, and oral phenotypic variables potentially relevant to rituximab response. Variables were considered according to biological rationale, available evidence, temporal availability, and potential use in pretreatment prognosis or post-treatment response monitoring. No patient-level dataset was analyzed, and no model was trained or validated. Results: Candidate pharmacogenomic variables include FCGR3A rs396991, FCGR2A rs1801274, and IL6 rs1800795, although evidence derives mainly from autoimmune diseases other than PV. IL10, TNF, TNFRSF13B, and population-specific HLA variables remain exploratory. Anti-desmoglein autoantibodies, BAFF, cytokine profiles, CD19⁺ B-cell depletion, CD27⁺ memory B-cell repopulation, regulatory T-cell dynamics, natural killer cell phenotypes, and clinical and oral phenotypic variables may provide complementary information, but none currently demonstrates sufficient independent predictive validity. The proposed RTX-AI Response Framework distinguishes the Pretreatment Prediction Component from the Post-Treatment Response Monitoring Component and assigns no numerical weights, probabilities, thresholds, or treatment recommendations. Conclusions: The framework should be interpreted exclusively as a conceptual research architecture. Prospective multicenter data collection, standardized biomarker assessment, task-specific model development, calibration, external validation, and evaluation of clinical utility are required before potential clinical implementation.