Erosive and Bullous Oral Lesions: Diagnostic Challenges and Clinical Algorithms

Introduction Diagnosing oral ulcers is challenging due to their nonspecific symptoms and variable microscopic/histopathological features, which lead to low sensitivity in immunological tests and significant diagnostic delays. Therefore, we aimed to review the existing literature to present current approaches to this problem. Methods We conducted a non-systematic review of existing guidelines for the differential diagnosis of oral lesions and published diagnostic algorithms using the PubMed database. The search strategy included the keywords as follows: “oral lichen planus” combined with “diagnosis”, “differential diagnosis”, “guidelines”, “AI”, “artificial intelligence”, or “machine learning”. Results There are few official guidelines for the diagnosis of oral lichen planus, and most of the existing ones are either too general, focused primarily on treatment, lacking decision-making algorithms, or limited to specific conditions. Discussion In the absence of comprehensive recommendations, independent authors have proposed diagnostic algorithms; however, these have been considered either insufficiently detailed or overly cautious. Neural networks demonstrate high accuracy in classifying oral lesions, but there are issues with overfitting and the limited dissemination of tools developed by individual research teams. These tools are classified as medical devices and require proper authorization. Therefore, we developed our own diagnostic “roadmap”, integrating patient history, histopathological evaluation, and immunochemical testing, along with a practical summary tailored for general practitioners. Conclusion Until comprehensive official guidelines addressing the diagnosis of oral lesions are introduced and AI-based tools are approved and commercialized, diagnostic schemes, such as the one presented here, may serve as helpful adjuncts for physicians.